Evaluation of the solitary pulmonary nodule: size matters, but do not ignore the power of morphology. Insights Imaging. 2018;9:73-86. [PMID: 29143191 PMCID: PMC5825309 DOI: 10.1007/s13244-017-0581-2]

Automated pulmonary nodule classification from low-dose CT images using ERBNet: an ensemble learning approach

The aim of this study was to develop a deep learning method for analyzing CT images with varying doses and qualities, aiming to categorize lung lesions into nodules and non-nodules. This study utilized the lung nodule analysis 2016 challenge dataset. Different low-dose CT (LDCT) images, including 10%, 20%, 40%, and 60% levels, were generated from the full-dose CT (FDCT) images. Five different 3D convolutional networks were developed to classify lung nodules from LDCT and reference FDCT images. The models were evaluated using 400 nodule and 400 non-nodule samples. An ensemble model was also developed to achieve a generalizable model across different dose levels. The model achieved an accuracy of 97.0% for nodule classification on FDCT images. However, the model exhibited relatively poor performance (60% accuracy) on LDCT images, indicating that dedicated models should be developed for each low-dose level. Dedicated models for handling LDCT led to dramatic increases in the accuracy of nodule classification. The dedicated low-dose models achieved a nodule classification accuracy of 90.0%, 91.1%, 92.7%, and 93.8% for 10%, 20%, 40%, and 60% of FDCT images, respectively. The accuracy of the deep learning models decreased gradually by almost 7% as LDCT images proceeded from 100 to 10%. However, the ensemble model led to an accuracy of 95.0% when tested on a combination of various dose levels. We presented an ensemble 3D CNN classifier for lesion classification, utilizing both LDCT and FDCT images. This model is able to analyze a combination of CT images with different dose levels and image qualities.

Lung cancer detection by electronic nose analysis of exhaled breath: a multicentre prospective external validation study

BACKGROUND

Electronic nose (eNose) analysis of exhaled breath shows potential for accurate and timely lung cancer diagnosis, yet prospective external validation studies are lacking. Our study primarily aimed to prospectively and externally validate a published eNose model for lung cancer detection in chronic obstructive pulmonary disease (COPD) patients and assess its diagnostic performance alongside a new eNose model, specifically tailored to the target population, in a more general outpatient population.

PATIENTS AND METHODS

This multicentre prospective external validation study included adults with clinical and/or radiological suspicion of lung cancer who were recruited from thoracic oncology outpatient clinics of two sites in the Netherlands. Breath profiles were collected using a cloud-connected eNose (SpiroNose®). The diagnostic performance of the original and new eNose models was assessed in various population subsets based on receiver operating characteristic-area under the curve (ROC-AUC), specificity, positive predictive value (PPV), and negative predictive value (NPV), targeting 95% sensitivity. For the new eNose model, a training cohort and a validation cohort were used.

RESULTS

Between March 2019 and November 2023, 364 participants were included. The original eNose model detected lung cancer with an ROC-AUC of 0.92 [95% confidence interval (CI) 0.85-0.99] in COPD patients (n = 98/116; 84%) and 0.80 (95% CI 0.75-0.85) in all participants (n = 216/364; 59%). At 95% sensitivity, the specificity, PPV, and NPV, were 72% and 51%, 95% and 74%, and 72% and 88%, respectively. In the validation cohort, the new eNose model identified lung cancer across all participants (n = 72/121; 60%) with an ROC-AUC of 0.83 (95% CI 0.75-0.91), sensitivity of 94%, specificity of 63%, PPV of 79%, and NPV of 89%. Notably, accurate detection was consistent across tumour characteristics, disease stage, diagnostic centres, and clinical characteristics.

CONCLUSION

This multicentre prospective external validation study confirms that eNose analysis of exhaled breath enables accurate lung cancer detection at thoracic oncology outpatient clinics, irrespective of tumour characteristics, disease stage, diagnostic centre, and clinical characteristics.

Development of a PET-CT Based Radiomics Model for Preoperative Prediction of the Novel IASLC Grading and Prognosis in Patients with Clinical Stage I Pure Solid Invasive Lung Adenocarcinoma

RATIONALE AND OBJECTIVES

To develop and validate a fluorine-18-fludeoxyglucose (18F-FDG) PET/CT-based radiomics nomogram for preoperative prediction of the International Association for the Study of Lung Cancer (IASLC) grading and recurrence-free survival (RFS) in patients with clinical stage I pure-solid invasive lung adenocarcinoma (LADC). MATERIALS AND METHODS: 418 patients with clinical stage I pure-solid invasive LADC who underwent preoperative 18F-FDG PET/CT examination were retrospectively enrolled. All patients were separated into the low-grade group (grade I and II; n=315) and the high-grade group (grade III; n=103) according to the IASLC grading system, and the cohort was randomly divided into a training set (n=292) and a testing set (n=126) at a ratio of 7:3. Radiomics features were extracted from CT and PET images in regions of the entire tumor. Multivariate analysis identified the independent predictors for IASLC grading and RFS. The Radscore, along with clinical and radiological features were combined to establish a predictive nomogram.

RESULTS

The ultimate Radiomics model, achieving AUCs of 0.838 and 0.768 in the training and testing sets. The multivariate logistic regression showed that higher maximum standard uptake value (SUVmax), cavity presence are the independent risk factors for IASLC grading. The integrated nomogram showed superior prediction performance than CT model (p=0.001) and PET model (p=0.028) in the training set. Furthermore, both pathological grade and preoperatively predictive IASLC grade derived by nomogram significantly stratified patients for RFS, with 5-year survival rates showing marked differences between low-grade and high-grade LADC (p<0.001).

CONCLUSION

The preoperative PET/CT-based radiomics nomogram represents a potential biomarker for predicting IASLC grade and RFS in patients with clinical stage I pure-solid invasive LADC.

Clinical and Computed Tomography Characteristics of Inflammatory Solid Pulmonary Nodules with Morphology Suggesting Malignancy

RATIONALE AND OBJECTIVES

To investigate the clinical and computed tomography characteristics of inflammatory solid pulmonary nodules (SPNs) with morphology suggesting malignancy, hereinafter referred to as atypical inflammatory SPNs (AI-SPNs).

MATERIALS AND METHODS

The CT data of 515 patients with SPNs who underwent surgical resection were retrospectively analyzed. These patients were divided into inflammatory and malignant groups and their clinical and imaging features were compared. Binary logistic regression analysis was performed to identify the independent factors for diagnosing AI-SPNs. An external validation cohort included 133 consecutive patients to test the model's predictive efficiency.

RESULTS

Univariate analysis showed that age < 62 years, male sex, maximum spiculation length > 9 mm, polygonal shapes, three-planar ratio > 1.48, Lung window/mediastinal window (L/M) ratio > 1.13, pleural tag type I, satellite lesions, and halo sign were more frequent in AI-SPNs, whereas pleural tag type III, bronchial truncation, and perifocal fibrosis were more common in malignant SPNs (M-SPNs) (all P < 0.05). Binary logistic regression showed age < 62 years, male sex, polygonal shape, three-planar ratio > 1.48, L/M ratio > 1.13, pleural tag type I, satellite lesions, halo sign, and absence of bronchial truncation were independent factors for diagnosing AI-SPNs (AUC, sensitivity, specificity, and accuracy of 0.951, 83.30%, 92.30%, and 87.20%, respectively). In the external validation cohort, the AUC, sensitivity, specificity, and accuracy were 0.969, 90.47%, 90.00%, and 90.23%, respectively.

CONCLUSION

AI-SPNs and M-SPNs exhibited different clinical and imaging characteristics. A good understanding of these differences may help reduce diagnostic errors in AI-SPNs and enable to choose an optimal treatment strategy.

Creation of nomograms that combine clinical, CT, and radiographic features to separate benign from malignant diseases using spiculation or (and) lobulation signs

BACKGROUND

Distinguishing between benign and malignant pulmonary nodules based on CT imaging features such as the spiculation sign and/or lobulation sign remains challenging and these nodules are often misinterpreted as malignant tumors. this retrospective study aimed to develop a prediction model to estimate the likelihood of benign and malignant lung nodules exhibiting spiculation and/or lobulation signs.

METHODS

A total of 500 patients with pulmonary nodules from June 2022 to August 2024 were retrospectively analyzed. Among them, 190 patients with spiculation sign and lobar sign or both on CT scan were included in this study. This investigation collected the clinical information, preoperative chest CT imaging characteristics, and postoperative histopathologic results from patients.Univariate and multivariate logistic regression analyses were employed to identify independent risk factors, from which a prediction model and nomogram were developed. In addition, The model performance was assessed through receiver operating characteristic(ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA).

RESULTS

In our study, 190 patients with pulmonary nodules underwent lung biopsy in 10 patients and surgical resection in 180 patients, of whom 53 were benign nodules and 137 were malignant nodules. When combined with the spiculation sign or (and) the lobulation sign, the vascular cluster sign, bronchial architectural distortion, bubble-like translucent area, nodule density, and CEA were found to be significant independent predictors for determining the benignity and malignancy of pulmonary nodules. The nomogram prediction model demonstrated high predictive accuracy with an area under the ROC curve (AUC) of 0.904. Furthermore, the model's calibration curve demonstrated adequate calibration. DCA confirmed the prediction model's validity.

CONCLUSION

The model can assist clinicians in making more accurate preoperative diagnoses and in guiding clinical decision-making regarding treatment, potentially reducing unnecessary surgical interventions.

Clinical Versus Pathological Staging in Patients with Resected Ground Glass Pulmonary Lesions

BACKGROUND

A ground glass nodule (GGN) is a radiologically descriptive term for a lung parenchymal area with increased attenuation and preserved bronchial and vascular structures. GGNs are further divided into pure versus subsolid lesions. The differential diagnosis for GGNs is wide and contains a malignant possibility for a lung adenocarcinoma precursor or tumor. Clinical and pathological staging of GGNs is based on the lesions' solid component and falls into a specific classification including T0 for TIS, T1mi for minimally invasive adenocarcinoma (MIA) and T1abc for lepidic predominant adenocarcinoma (LPA) according to the eighth edition of the TNM classification of lung cancer. Correlation between solid parts seen on a CT scan and the tumor pathological invasive component is not absolute.

METHODS

This retrospective study collected the data of 68 GGNs that were operated upon in Carmel Medical Center. A comparison between preoperative clinical staging and post-surgery pathological staging was conducted.

RESULTS

Over a third of the lesions, twenty-four (35.3%), were upstaged while only four (5.9%) lesions were downstaged. Another third of the lesions, twenty-three (33.8%), kept their stage. In three (4.4%) cases, premalignant lesion atypical adenomatous hyperplasia (AAH) was diagnosed. Ten (14.7%) cases were diagnosed as non-malignant on final pathology. These findings show an overall low agreement between the clinical and pathological stages of GGNs.

CONCLUSIONS

The relatively high percentage of upstaging tumors detected in this study and the overall safe and short surgical procedure advocate for surgical resection even in the presence of a significant number of non-malignant lesions that retrospectively do not mandate intervention at all.

CT-Guided Transthoracic Core-Needle Biopsy of Pulmonary Nodules: Current Practices, Efficacy, and Safety Considerations

CT-guided transthoracic core-needle biopsy (CT-TTNB) is a minimally invasive procedure that plays a crucial role in diagnosing pulmonary nodules. With high diagnostic yield and low complication rates, CT-TTNB is favored over traditional surgical biopsies, providing accuracy in detecting both malignant and benign conditions. This literature review aims to present a comprehensive overview of CT-TTNB, focusing on its indications, procedural techniques, diagnostic yield, and safety considerations. Studies published between 2013 and 2024 were systematically reviewed from PubMed, Web of Science, Scopus, and Cochrane Library using the SANRA methodology. The results highlight that CT-TTNB has a diagnostic yield of 85-95% and sensitivity rates for detecting malignancies between 92 and 97%. Several factors, including nodule size, lesion depth, needle passes, and imaging techniques, influence diagnostic success. Complications such as pneumothorax and pulmonary hemorrhage were noted, with incidence rates varying from 12 to 45% for pneumothorax and 4 to 27% for hemorrhage. Preventative strategies and management algorithms are essential for minimizing and addressing these risks. In conclusion, CT-TTNB remains a reliable and effective method for diagnosing pulmonary nodules, particularly in peripheral lung lesions. Advancements such as PET/CT fusion imaging, AI-assisted biopsy planning, and robotic systems further enhance precision and safety. This review emphasizes the importance of careful patient selection and procedural planning to maximize outcomes while minimizing risks, ensuring that CT-TTNB continues to be an indispensable tool in pulmonary diagnostics.

Factors associated with the distribution of brain metastases in lung cancer: a retrospective study

The distribution of brain metastases (BMs) in patients with lung cancer may be associated with the primary tumor-related factors and cerebral small vascular diseases (CSVDs). The aim of this study was to investigate the potential effects of the above factors on the distribution of BMs. A total of 5,788 lesions in 823 patients with BMs from lung cancer were enrolled. The numbers of BMs and CSVDs in 15 brain regions were determined. CSVDs include recent small subcortical infarcts (RSSIs), perivascular spaces, and lacunes of presumed vascular origin (LPVOs). We collected the number of CSVDs, and primary tumor-related factors (including clinical and imaging features) in lung cancer patients with BMs. Univariate and multivariate linear regression were utilized to analyze the potential influence of the above factors on the number of BMs in 15 brain regions. In addition, we performed subgroup analyses of all patients with adenocarcinoma (AD), female patients with AD, male patients with AD, and patients with small cell lung cancer. Univariate linear regression analyses showed that bone metastasis, adrenal metastasis, RSSIs, and LPVOs were associated with the number of BMs in over half of the examined brain regions. Only the independent association of LVPOs persisted in the multivariate linear regression analyses, and similar phenomenon was found in the subgroup analyses. In conclusion, the distribution of BMs in lung cancer patients appears to be associated with the presence of LVPOs, while primary tumor-related factors have less influence.

Challenging the significance of SUV-based parameters in a large-scale retrospective study on lung lesions

BACKGROUND

Although many well-known factors affect the maximum standardized uptake value (SUVmax), it remains the most requested and used parameter, especially among clinicians, despite other parameters, such as the standardized uptake value corrected for lean body mass and the metabolic tumor volume, being proven to be less sensitive to the same factors, more robust, and eventually more informative. This study intends to provide robust evidence regarding the diagnostic and prognostic value of SUVmax in a large cohort of subjects with suspected malignant lung nodules imaged by [18F]FDG PET/CT.

MATERIALS AND METHODS

We performed a retrospective analysis of patients with suspected/confirmed primary lung tumours undergoing [18F]FDG PET/CT. The sample size was 567 patients. Demographics, imaging, surgical, histological, and follow-up data were collected. SUVmax was analysed according to histology, stage, scanner, and outcome. The impact on measured values of different reconstruction protocols was assessed. All potential predictors of patients' outcome were assessed.

RESULTS

91% cases were primary lung tumours. Lung benign nodules or metastases accounted for 5% and 4% of cases. Most patients presented with adenocarcinoma (70%) and stage I disease (51%); 144 patients relapsed and 55 died. SUVmax failed to effectively differentiate benign lesions from primary tumours or metastases. Stage I patients presented lower SUVmax. SUVmax significantly correlated with patient weight, injected [18F]FDG activity, and lesion size and differed between reconstructions' protocols. Survival analyses revealed no independent prognostic significance for SUVmax in progression-free after adjusting for other variables. SUVmax correlated with overall survival, disease stage and tumour histotype.

CONCLUSION

Our study confirms that SUVmax, though widely employed, present relevant limitations in discriminating between benign lesion and lung cancer, in classifying cancer histotypes, and in predicting patient outcomes independently. Known influencing factors significantly impact on numerical values, thus SUV values should be regarded with caution in clinical practice.

An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images

BACKGROUND

This study aims to assess the performance of an established an AI algorithm trained on conventional polychromatic computed tomography (CT) images (CPIs) to detect pulmonary ground-glass nodules (GGNs) on virtual monochromatic images (VMIs), and to screen the optimal virtual monochromatic energy for the clinical evaluation of GGNs.

METHODS

Non-enhanced chest SDCT images of patients with pulmonary GGNs in our clinic from January 2022 to December 2022 were continuously collected: adenocarcinoma in situ (AIS, n = 40); minimally invasive adenocarcinoma (MIA, n = 44) and invasive adenocarcinoma (IAC, n = 46). A commercial CAD system based on deep convolutional neural networks (DL-CAD) was used to process the CPIs, 40, 50, 60, 70, and 80 keV monochromatic images of 130 spectral CT images. AI-based histogram parameters by logistic regression analysis. The diagnostic performance was evaluated by the receiver operating characteristic (ROC) curves, and Delong's test was used to compare the CPIs group with the VMIs group.

RESULTS

When distinguishing IAC from MIA, the diagnostic efficiency of total mass was obtained at 80 keV, which was superior to those of other energy levels (P < 0.05). And Delong's test indicated that the differences between the area-under-the-curve (AUC) values of the CPIs group and the VMIs group were not statistically significant (P > 0.05).

CONCLUSION

The AI algorithm trained on CPIs showed consistent diagnostic performance on VMIs. When pulmonary GGNs are encountered in clinical practice, 80 keV could be the optimal virtual monochromatic energy for the identification of preoperative IAC on a non-enhanced chest CT.

Multi-View Soft Attention-Based Model for the Classification of Lung Cancer-Associated Disabilities

Background: The detection of lung nodules at their early stages may significantly enhance the survival rate and prevent progression to severe disability caused by advanced lung cancer, but it often requires manual and laborious efforts for radiologists, with limited success. To alleviate it, we propose a Multi-View Soft Attention-Based Convolutional Neural Network (MVSA-CNN) model for multi-class lung nodular classifications in three stages (benign, primary, and metastatic). Methods: Initially, patches from each nodule are extracted into three different views, each fed to our model to classify the malignancy. A dataset, namely the Lung Image Database Consortium Image Database Resource Initiative (LIDC-IDRI), is used for training and testing. The 10-fold cross-validation approach was used on the database to assess the model's performance. Results: The experimental results suggest that MVSA-CNN outperforms other competing methods with 97.10% accuracy, 96.31% sensitivity, and 97.45% specificity. Conclusions: We hope the highly predictive performance of MVSA-CNN in lung nodule classification from lung Computed Tomography (CT) scans may facilitate more reliable diagnosis, thereby improving outcomes for individuals with disabilities who may experience disparities in healthcare access and quality.

A multiscale 3D network for lung nodule detection using flexible nodule modeling

BACKGROUND

Lung cancer is the most common type of cancer. Detection of lung cancer at an early stage can reduce mortality rates. Pulmonary nodules may represent early cancer and can be identified through computed tomography (CT) scans. Malignant risk can be estimated based on attributes like size, shape, location, and density.

PURPOSE

Deep learning algorithms have achieved remarkable advancements in this domain compared to traditional machine learning methods. Nevertheless, many existing anchor-based deep learning algorithms exhibit sensitivity to predefined anchor-box configurations, necessitating manual adjustments to obtain optimal outcomes. Conversely, current anchor-free deep learning-based nodule detection methods normally adopt fixed-size nodule models like cubes or spheres.

METHODS

To address these technical challenges, we propose a multiscale 3D anchor-free deep learning network (M3N) for pulmonary nodule detection, leveraging adjustable nodule modeling (ANM). Within this framework, ANM empowers the representation of target objects in an anisotropic manner, with a novel point selection strategy (PSS) devised to accelerate the learning process of anisotropic representation. We further incorporate a composite loss function that combines the conventional L2 loss and cosine similarity loss, facilitating M3N to learn nodules' intensity distribution in three dimensions.

RESULTS

Experiment results show that the M3N achieves 90.6% competitive performance metrics (CPM) with seven predefined false positives per scan on the LUNA 16 dataset. This performance appears to exceed that of other state-of-the-art deep learning-based networks reported in their respective publications. Individual test results also demonstrate that M3N excels in providing more accurate, adaptive bounding boxes surrounding the contours of target nodules.

CONCLUSIONS

The newly developed nodule detection system reduces reliance on prior knowledge, such as the general size of objects in the dataset, thus it should enhance overall robustness and versatility. Distinct from traditional nodule modeling techniques, the ANM approach aligns more closely with the morphological characteristics of nodules. Time consumption and detection results demonstrate promising efficiency and accuracy which should be validated in clinical settings.

Predicting malignant potential of solitary pulmonary nodules in patients with COVID-19 infection: a comprehensive analysis of CT imaging and tumor markers

OBJECTIVE

To analyze the value of combining computed tomography (CT) with serum tumor markers in the differential diagnosis of benign and malignant solitary pulmonary nodules (SPNs).

METHODS

The case data of 267 patients diagnosed with SPNs in the First Affiliated Hospital of Zhengzhou University from March 2020 to January 2023 were retrospectively analyzed. All individuals diagnosed with coronavirus disease 2019 (COVID-19) were confirmed via respiratory specimen viral nucleic acid testing. The included cases underwent CT, serum tumor marker testing and pathological examination. The diagnostic efficacy and clinical significance of CT, serum tumor marker testing and a combined test in identifying benign and malignant SPNs were analyzed using pathological histological findings as the gold standard. Finally, a nomogram mathematical model was established to predict the malignant probability of SPNs.

RESULTS

Of the 267 patients with SPNs, 91 patients were not afflicted with COVID-19, 36 exhibited malignant characteristics, whereas 55 demonstrated benign features. Conversely, within the cohort of 176 COVID-19 patients presenting with SPNs, 62 were identified as having malignant SPNs, and the remaining 114 were diagnosed with benign SPNs. CT scans revealed statistically significant differences between the benign and malignant SPNs groups in terms of CT values (P<0.001), maximum nodule diameter (P<0.001), vascular convergence sign (P<0.001), vacuole sign (P = 0.0007), air bronchogram sign (P = 0.0005), and lobulation sign (P = 0.0005). Malignant SPNs were associated with significantly higher levels of carcinoembryonic antigen (CEA) and neuron-specific enolase (NSE) compared to benign SPNs (P < 0.05), while no significant difference was found in carbohydrate antigen 125 (CA125) levels (P = 0.054 for non-COVID-19; P = 0.072 for COVID-19). The sensitivity (95.83%), specificity (95.32%), and accuracy (95.51%) of the comprehensive diagnosis combining serum tumor markers and CT were significantly higher than those of CT alone (70.45%, 79.89%, 76.78%) or serum tumor marker testing alone (56.52%, 73.71%, 67.79%) (P < 0.05). A visual nomogram predictive model for malignant pulmonary nodules was constructed.

CONCLUSION

Combining CT with testing for CEA, CA125, and NSE levels offers high diagnostic accuracy and sensitivity, enables precise differentiation between benign and malignant nodules, particularly in the context of COVID-19, thereby reducing the risk of unnecessary surgical interventions.

Vascular Biomarkers for Pulmonary Nodule Malignancy: Arteries vs. Veins

OBJECTIVE

This study aims to investigate the association between the arteries and veins surrounding a pulmonary nodule and its malignancy.

METHODS

A dataset of 146 subjects from a LDCT lung cancer screening program was used in this study. AI algorithms were used to automatically segment and quantify nodules and their surrounding macro-vasculature. The macro-vasculature was differentiated into arteries and veins. Vessel branch count, volume, and tortuosity were quantified for arteries and veins at different distances from the nodule surface. Univariate and multivariate logistic regression (LR) analyses were performed, with a special emphasis on the nodules with diameters ranging from 8 to 20 mm. ROC-AUC was used to assess the performance based on the k-fold cross-validation method. Average feature importance was evaluated in several machine learning models.

RESULTS

The LR models using macro-vasculature features achieved an AUC of 0.78 (95% CI: 0.71-0.86) for all nodules and an AUC of 0.67 (95% CI: 0.54-0.80) for nodules between 8-20 mm. Models including macro-vasculature features, demographics, and CT-derived nodule features yielded an AUC of 0.91 (95% CI: 0.87-0.96) for all nodules and an AUC of 0.82 (95% CI: 0.71-0.92) for nodules between 8-20 mm. In terms of feature importance, arteries within 5.0 mm from the nodule surface were the highest-ranked among macro-vasculature features and retained their significance even with the inclusion of demographics and CT-derived nodule features.

CONCLUSIONS

Arteries within 5.0 mm from the nodule surface emerged as a potential biomarker for effectively discriminating between malignant and benign nodules.

Imaging diagnostics of pulmonary ground-glass nodules: a narrative review with current status and future directions

Background and Objective

The incidence rate of lung cancer, which also has the highest mortality rates for both men and women worldwide, is increasing globally. Due to advancements in imaging technology and the growing inclination of individuals to undergo screening, the detection rate of ground-glass nodules (GGNs) has surged rapidly. Currently, artificial intelligence (AI) methods for data analysis and interpretation, image processing, illness diagnosis, and lesion prediction offer a novel perspective on the diagnosis of GGNs. This article aimed to examine how to detect malignant lesions as early as possible and improve clinical diagnostic and treatment decisions by identifying benign and malignant lesions using imaging data. It also aimed to describe the use of computed tomography (CT)-guided biopsies and highlight developments in AI techniques in this area.

Methods

We used PubMed, Elsevier ScienceDirect, Springer Database, and Google Scholar to search for information relevant to the article's topic. We gathered, examined, and interpreted relevant imaging resources from the Second Affiliated Hospital of Nanchang University's Imaging Center. Additionally, we used Adobe Illustrator 2020 to process all the figures.

Key Content and Findings

We examined the common signs of GGNs, elucidated the relationship between these signs and the identification of benign and malignant lesions, and then described the application of AI in image segmentation, automatic classification, and the invasiveness prediction of GGNs over the last three years, including its limitations and outlook. We also discussed the necessity of conducting biopsies of persistent pure GGNs.

Conclusions

A variety of imaging features can be combined to improve the diagnosis of benign and malignant GGNs. The use of CT-guided puncture biopsy to clarify the nature of lesions should be considered with caution. The development of new AI tools brings new possibilities and hope to improving the ability of imaging physicians to analyze GGN images and achieving accurate diagnosis.

Comparative analysis of medical glue and positioning hooks for preoperative localization of pulmonary nodules

Background

Through preoperative localization, surgeons can easily locate ground glass nodules (GGNs) and effectively control the extent of resection. Therefore, it is necessary to choose an appropriate puncture positioning method. The purpose of this study was to evaluate the effectiveness and safety of medical glue and positioning hooks in the preoperative positioning of GGNs and to provide a reference for clinical selection.

Methods

From March 30, 2020 to June 13, 2022, a total of 859 patients with a CT diagnosis of GGNs requiring surgical resection were included in our study at the hospital. Among them, 21 patients who either opted out or could not undergo preoperative localization for various reasons were excluded. Additionally, 475 patients who underwent preoperative localization using medical glue and 363 patients who underwent preoperative localization through positioning hooks were also excluded. We conducted statistical analyses on the baseline data, success rates, complications, and pathological results of the remaining patients. The success rates, complication rates, and pathological results were compared between the two groups-those who received medical glue localization and those who received positioning hook localization.

Results

There was no statistically significant difference between the two groups of patients in terms of age, body mass index, smoking history, location of the nodule, distance of the nodule from the pleura, or postoperative pathological results (P > 0.05). The success rate of medical glue and positioning hooks was 100%. The complication rates of medical glue and positioning hooks during single nodule positioning were 39.18% and 23.18%, respectively, which were significantly different (p < 0.001); the complication rates during multiple nodule positioning were 49.15% and 49.18%, respectively, with no statistically significant differences (p > 0.05). In addition, the method of positioning and the clinical characteristics of the patients were not found to be independent risk factors for the occurrence of complications. The detection rate of pulmonary nodules also showed some positive correlation with the spread of COVID-19 during the 2020-2022 period when COVID-19 was prevalent.

Conclusion

When positioning a single node, the safety of positioning hooks is greater than when positioning multiple nodes, the safety of medical glue and positioning hooks is comparable, and the appropriate positioning method should be chosen according to the individual situation of the patient.

Identification of CT Features to Differentiate Pulmonary Sarcoma from Carcinoma

Background  Primary lung sarcoma (PLS) differs in management protocols and prognosis from the more common primary lung carcinoma (PLC). It becomes imperative to raise a high index of suspicion on radiological and pathological features. Purpose  The aim of this study is to highlight the variable imaging appearances of PLS compared with PLC, which impacts radiologic - pathologic correlation. Materials and Methods  A retrospective observational study of 68 patients with biopsy-proven lung tumors who underwent baseline imaging at our tertiary care cancer hospital was conducted between January 2018 and March 2022. The patient details and imaging parameters of the mass on contrast-enhanced computed tomography (CECT) were recorded and analyzed for patients with PLS and compared with PLC. Follow-up imaging was available in 9/12 PLS and 52/56 PLC patients. Results  Among 12 patients with PLS, 5 patients had synovial sarcoma on histopathology. PLS was seen in patients with a mean age of 40.8 years; the mass showed a mean size of 13.2 cm, lower lobe (75%), parahilar (75%), hilar involvement (41.7%), oval shape (41.7%), circumscribed (25%) or lobulated (75%) margins, lower mean postcontrast attenuation of 57.3 HU, fissural extension (50%), calcification (50%), and no organ metastasis other than to the lung. PLC (56 patients) was seen in the elderly with a mean age of 54.8 years; the mass showed a mean size of 5.7 cm, irregular shape (83.9%), spiculated margins (73.2%), higher mean postcontrast attenuation (77.3 HU), chest wall infiltration (30.4%), and distant metastasis (58.9%) at baseline imaging. A statistically significant difference ( p  < 0.05) was seen between sarcoma and carcinoma in the mean age, size, site, shape, margins, postcontrast attenuation, presence of calcifications, fissural extension, and distant metastasis. Conclusion  The distinct imaging features of sarcoma help in differentiating it from carcinoma. This can also be used to corroborate with histopathology to achieve concordance and guide clinicians on further approach.

A nomogram combining CT-based radiomic features with clinical features for the differentiation of benign and malignant cystic pulmonary nodules

BACKGROUND

Currently, the differentiation between benign and malignant cystic pulmonary nodules poses a significant challenge for clinicians. The objective of this retrospective study was to construct a predictive model for determining the likelihood of malignancy in patients with cystic pulmonary nodules.

METHODS

The current study involved 129 patients diagnosed with cystic pulmonary nodules between January 2017 and June 2023 at the Neijiang First People's Hospital. The study gathered the clinical data, preoperative imaging features of chest CT, and postoperative histopathological results for both cohorts. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors, from which a prediction model and nomogram were developed. In addition, The model's performance was assessed through receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA).

RESULTS

A cohort of 129 patients presenting with cystic pulmonary nodules, consisting of 92 malignant and 37 benign lesions, was examined. Logistic data analysis identified a cystic airspace with a mural nodule, spiculation, mural morphology, and the number of cystic cavities as significant independent predictors for discriminating between benign and malignant cystic lung nodules. The nomogram prediction model demonstrated a high level of predictive accuracy, as evidenced by an area under the ROC curve (AUC) of 0.874 (95% CI: 0.804-0.944). Furthermore, the calibration curve of the model displayed satisfactory calibration. DCA proved that the prediction model was useful for clinical application.

CONCLUSION

In summary, the risk prediction model for benign and malignant cystic pulmonary nodules has the potential to assist clinicians in the diagnosis of such nodules and enhance clinical decision-making processes.

Integrating Machine Learning in Clinical Practice for Characterizing the Malignancy of Solitary Pulmonary Nodules in PET/CT Screening

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.

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.

Absolute ground truth-based validation of computer-aided nodule detection and volumetry in low-dose CT imaging

PURPOSE

To validate the performance of computer-aided detection (CAD) and volumetry software using an anthropomorphic phantom with a ground truth (GT) set of 3D-printed nodules.

METHODS

The Kyoto Kaguku Lungman phantom, containing 3D-printed solid nodules including six diameters (4 to 9 mm) and three morphologies (smooth, lobulated, spiculated), was scanned at varying CTDIvol levels (6.04, 1.54 and 0.20 mGy). Combinations of reconstruction algorithms (iterative and deep learning image reconstruction) and kernels (soft and hard) were applied. Detection, volumetry and density results recorded by a commercially available AI-based algorithm (AVIEW LCS + ) were compared to the absolute GT, which was determined through µCT scanning at 50 µm resolution. The associations between image acquisition parameters or nodule characteristics and accuracy of nodule detection and characterization were analyzed with chi square tests and multiple linear regression.

RESULTS

High levels of detection sensitivity and precision (minimal 83 % and 91 % respectively) were observed across all acquisitions. Neither reconstruction algorithm nor radiation dose showed significant associations with detection. Nodule diameter however showed a highly significant association with detection (p < 0.0001). Volumetric measurements for nodules > 6 mm were accurate within 10 % absolute range from volumeGT, regardless of dose and reconstruction. Nodule diameter and morphology are major determinants of volumetric accuracy (p < 0.001). Density assignment was not significantly influenced by any parameters.

CONCLUSIONS

Our study confirms the software's accurate performance in nodule volumetry, detection and density characterization with robustness for variations in CT imaging protocols. This study suggests the incorporation of similar phantom setups in quality assurance of CAD tools.

Slowly Growing Pulmonary Glandular Papilloma with Air Bronchogram: A Case Report

Pulmonary glandular papilloma is a rare benign neoplasm that has not been studied extensively. This neoplasm presents as a solid nodule, consolidation, or mass, with or without atelectasis, and assessing the correlation between these findings and the risk of malignancy is challenging. A 60-year-old woman presented a solitary pulmonary nodule on screening chest radiography and chest computed tomography (CT). During the subsequent 2-year follow-up, CT showed a progressive increase in nodule size and an air bronchogram, suggesting malignancy. The patient underwent a right upper lobectomy, and the final diagnosis was glandular papilloma. Teaching point: Pulmonary glandular papilloma with growth and an air bronchogram.

Synchrotron Radiation Refraction-Contrast Computed Tomography Based on X-ray Dark-Field Imaging Optics of Pulmonary Malignancy: Comparison with Pathologic Examination

Refraction-contrast computed tomography based on X-ray dark-field imaging (XDFI) using synchrotron radiation (SR) has shown superior resolution compared to conventional absorption-based methods and is often comparable to pathologic examination under light microscopy. This study aimed to investigate the potential of the XDFI technique for clinical application in lung cancer diagnosis. Two types of lung specimens, primary and secondary malignancies, were investigated using an XDFI optic system at beamline BL14B of the High-Energy Accelerator Research Organization Photon Factory, Tsukuba, Japan. Three-dimensional reconstruction and segmentation were performed on each specimen. Refraction-contrast computed tomographic images were compared with those obtained from pathological examinations. Pulmonary microstructures including arterioles, venules, bronchioles, alveolar sacs, and interalveolar septa were identified in SR images. Malignant lesions could be distinguished from the borders of normal structures. The lepidic pattern was defined as the invasive component of the same primary lung adenocarcinoma. The SR images of secondary lung adenocarcinomas of colorectal origin were distinct from those of primary lung adenocarcinomas. Refraction-contrast images based on XDFI optics of lung tissues correlated well with those of pathological examinations under light microscopy. This imaging method may have the potential for use in lung cancer diagnosis without tissue damage. Considerable equipment modifications are crucial before implementing them from the lab to the hospital in the near future.

The geometric evolution of aortic dissections: Predicting surgical success using fluctuations in integrated Gaussian curvature

Clinical imaging modalities are a mainstay of modern disease management, but the full utilization of imaging-based data remains elusive. Aortic disease is defined by anatomic scalars quantifying aortic size, even though aortic disease progression initiates complex shape changes. We present an imaging-based geometric descriptor, inspired by fundamental ideas from topology and soft-matter physics that captures dynamic shape evolution. The aorta is reduced to a two-dimensional mathematical surface in space whose geometry is fully characterized by the local principal curvatures. Disease causes deviation from the smooth bent cylindrical shape of normal aortas, leading to a family of highly heterogeneous surfaces of varying shapes and sizes. To deconvolute changes in shape from size, the shape is characterized using integrated Gaussian curvature or total curvature. The fluctuation in total curvature (δK) across aortic surfaces captures heterogeneous morphologic evolution by characterizing local shape changes. We discover that aortic morphology evolves with a power-law defined behavior with rapidly increasing δK forming the hallmark of aortic disease. Divergent δK is seen for highly diseased aortas indicative of impending topologic catastrophe or aortic rupture. We also show that aortic size (surface area or enclosed aortic volume) scales as a generalized cylinder for all shapes. Classification accuracy for predicting aortic disease state (normal, diseased with successful surgery, and diseased with failed surgical outcomes) is 92.8±1.7%. The analysis of δK can be applied on any three-dimensional geometric structure and thus may be extended to other clinical problems of characterizing disease through captured anatomic changes.

Development of a combined radiomics and CT feature-based model for differentiating malignant from benign subcentimeter solid pulmonary nodules

BACKGROUND

We aimed to develop a combined model based on radiomics and computed tomography (CT) imaging features for use in differential diagnosis of benign and malignant subcentimeter (≤ 10 mm) solid pulmonary nodules (SSPNs).

METHODS

A total of 324 patients with SSPNs were analyzed retrospectively between May 2016 and June 2022. Malignant nodules (n = 158) were confirmed by pathology, and benign nodules (n = 166) were confirmed by follow-up or pathology. SSPNs were divided into training (n = 226) and testing (n = 98) cohorts. A total of 2107 radiomics features were extracted from contrast-enhanced CT. The clinical and CT characteristics retained after univariate and multivariable logistic regression analyses were used to develop the clinical model. The combined model was established by associating radiomics features with CT imaging features using logistic regression. The performance of each model was evaluated using the area under the receiver-operating characteristic curve (AUC).

RESULTS

Six CT imaging features were independent predictors of SSPNs, and four radiomics features were selected after a dimensionality reduction. The combined model constructed by the logistic regression method had the best performance in differentiating malignant from benign SSPNs, with an AUC of 0.942 (95% confidence interval 0.918-0.966) in the training group and an AUC of 0.930 (0.902-0.957) in the testing group. The decision curve analysis showed that the combined model had clinical application value.

CONCLUSIONS

The combined model incorporating radiomics and CT imaging features had excellent discriminative ability and can potentially aid radiologists in diagnosing malignant from benign SSPNs.

RELEVANCE STATEMENT

The model combined radiomics features and clinical features achieved good efficiency in predicting malignant from benign SSPNs, having the potential to assist in early diagnosis of lung cancer and improving follow-up strategies in clinical work.

KEY POINTS

• We developed a pulmonary nodule diagnostic model including radiomics and CT features. • The model yielded the best performance in differentiating malignant from benign nodules. • The combined model had clinical application value and excellent discriminative ability. • The model can assist radiologists in diagnosing malignant from benign pulmonary nodules.

Research of correlation between personality traits and hormones with the nature of pulmonary nodules

Background

Rising rates of lung cancer screening have contributed to an increase in pulmonary nodule diagnosis rates. Studies have shown that psychosocial factors and hormones have an impact on the development of the oncological diseases. Therefore, we conducted this study to explore the potential relationship between pulmonary nodules pathology and patient personality traits and hormone levels.

Methods

This study enrolled 245 individuals who had first been diagnosed with pulmonary nodules in Tangdu Hospital and admitted for surgery. The personality profile of these patients was analyzed on admission using the C-Type Behavioral Scale and hormone levels were measured in preoperative serum samples. Associations between nodule pathology, personality scores, and hormone levels， were then assessed through Statistical methods analysis.

Results

Behavioral scale analyses revealed significant differences four items, including depression, anger outward, optimism, and social support (P< 0.05). Specifically, patients with higher depression scores were more likely to harbor malignant pulmonary nodules, as were patients with lower levels of anger outward, social support, and optimism. Univariate analyses indicated that nodule pathology was associated with significant differences in nodule imaging density, CT value, testosterone levels, and T4 levels(P< 0.05), and logistic regression analyses revealed pulmonary nodule imaging density and T4 levels to be significant differences of nodule pathology.

Conclusion

The results showed a significant association between nodules pathology and the personality characteristics of the patients (depression, anger outward, optimism, social support), the patients' T4 levels and the imaging density of the nodules.

Radiomics analysis from magnetic resonance imaging in predicting the grade of nonfunctioning pancreatic neuroendocrine tumors: a multicenter study

OBJECTIVES

To explore the potential of radiomics features to predict the histologic grade of nonfunctioning pancreatic neuroendocrine tumor (NF-PNET) patients using non-contrast sequence based on MRI.

METHODS

Two hundred twenty-eight patients with NF-PNETs undergoing MRI at 5 centers were retrospectively analyzed. Data from center 1 (n = 115) constituted the training cohort, and data from centers 2-5 (n = 113) constituted the testing cohort. Radiomics features were extracted from T2-weighted images and the apparent diffusion coefficient. The least absolute shrinkage and selection operator was applied to select the most important features and to develop radiomics signatures. The area under receiver operating characteristic curve (AUC) was performed to assess models.

RESULTS

Tumor boundary, enhancement homogeneity, and vascular invasion were used to construct the radiological model to stratify NF-PNET patients into grade 1 and 2/3 groups, which yielded AUC of 0.884 and 0.684 in the training and testing groups. A radiomics model including 4 features was constructed, with an AUC of 0.941 and 0.871 in the training and testing cohorts. The fusion model combining the radiomics signature and radiological characteristics showed good performance in the training set (AUC = 0.956) and in the testing set (AUC = 0.864), respectively.

CONCLUSION

The developed model that integrates radiomics features with radiological characteristics could be used as a non-invasive, dependable, and accurate tool for the preoperative prediction of grade in NF-PNETs.

CLINICAL RELEVANCE STATEMENT

Our study revealed that the fusion model based on a non-contrast MR sequence can be used to predict the histologic grade before operation. The radiomics model may be a new and effective biological marker in NF-PNETs.

KEY POINTS

The diagnostic performance of the radiomics model and fusion model was better than that of the model based on clinical information and radiological features in predicting grade 1 and 2/3 of nonfunctioning pancreatic neuroendocrine tumors (NF-PNETs). Good performance of the model in the four external testing cohorts indicated that the radiomics model and fusion model for predicting the grades of NF-PNETs were robust and reliable, indicating the two models could be used in the clinical setting and facilitate the surgeons' decision on risk stratification. The radiomics features were selected from non-contrast T2-weighted images (T2WI) and diffusion-weighted imaging (DWI) sequence, which means that the administration of contrast agent was not needed in grading the NF-PNETs.

Predicting High-Grade Patterns in Stage I Solid Lung Adenocarcinoma: A Study of 371 Patients Using Refined Radiomics and Deep Learning-Guided CatBoost Classifier

INTRODUCTION

This study aimed to devise a diagnostic algorithm, termed the Refined Radiomics and Deep Learning Features-Guided CatBoost Classifier (RRDLC-Classifier), and evaluate its efficacy in predicting pathological high-grade patterns in patients diagnosed with clinical stage I solid lung adenocarcinoma (LADC).

METHODS

In this retrospective study, a total of 371 patients diagnosed with clinical stage I solid LADC were randomly categorized into training and validation sets in a 7:3 ratio. Uni- and multivariate logistic regression analyses were performed to examine the imaging findings that can be used to predict pathological high-grade patterns meticulously. Employing redundancy and the least absolute shrinkage and selection operator regression, a radiomics model was developed. Subsequently, radiomics refinement and deep learning features were employed using a machine learning algorithm to construct the RRDLC-Classifier, which aims to predict high-grade patterns in clinical stage I solid LADC. Evaluation metrics, such as receiver operating characteristic curves, areas under the curve (AUCs), accuracy, sensitivity, and specificity, were computed for assessment.

RESULTS

The RRDLC-Classifier attained the highest AUC of 0.838 (95% confidence interval [CI]: 0.766-0.911) in predicting high-grade patterns in clinical stage I solid LADC, followed by radiomics with an AUC of 0.779 (95% CI: 0.675-0.883), and imaging findings with an AUC of 0.6 (95% CI: 0.472-0.726).

CONCLUSIONS

This study introduces the RRDLC-Classifier, a novel diagnostic algorithm that amalgamates refined radiomics and deep learning features to predict high-grade patterns in clinical stage I solid LADC. This algorithm may exhibit excellent diagnostic performance, which can facilitate its application in precision medicine.

Tumor blood vessel in 3D reconstruction CT imaging as an risk indicator for growth of pulmonary nodule with ground-glass opacity

OBJECTIVE

Despite the vital role of blood perfusion in tumor progression, in patients with persistent pulmonary nodule with ground-glass opacity (GGO) is still unclear. This study aims to investigate the relationship between tumor blood vessel and the growth of persistent malignant pulmonary nodules with ground-glass opacity (GGO).

METHODS

We collected 116 cases with persistent malignant pulmonary nodules, including 62 patients as stable versus 54 patients in the growth group, from 2017 to 2021. Three statistical methods of logistic regression model, Kaplan-Meier analysis regression analysis were used to explore the potential risk factors for growth of malignant pulmonary nodules with GGO.

RESULTS

Multivariate variables logistic regression analysis and Kaplan-Meier analysis identified that tumor blood vessel diameter (p = 0.013) was an significant risk factor in the growth of nodules and Cut-off value of tumor blood vessel diameter was 0.9 mm with its specificity 82.3% and sensitivity 66.7%.While in subgroup analysis, for the GGO CTR < 0.5[C(the maximum diameter of consolidation in tumor)/T(the maximum diameter of the whole tumor including GGO) ratio], tumor blood vessel diameter (p = 0.027) was important during the growing processes of nodules.

CONCLUSIONS

The tumor blood vessel diameter of GGO lesion was closely associated with the growth of malignant pulmonary nodules. The results of this study would provide evidence for effective follow-up strategies for pulmonary nodule screening.

Diagnostic performance of a deep learning-based method in differentiating malignant from benign subcentimeter (≤10 mm) solid pulmonary nodules

Background

This study assessed the diagnostic performance of a deep learning (DL)-based model for differentiating malignant subcentimeter (≤10 mm) solid pulmonary nodules (SSPNs) from benign ones in computed tomography (CT) images compared against radiologists with 10 and 15 years of experience in thoracic imaging (medium-senior seniority).

Methods

Overall, 200 SSPNs (100 benign and 100 malignant) were retrospectively collected. Malignancy was confirmed by pathology, and benignity was confirmed by follow-up or pathology. CT images were fed into the DL model to obtain the probability of malignancy (range, 0-100%) for each nodule. According to the diagnostic results, enrolled nodules were classified into benign, malignant, or indeterminate. The accuracy and diagnostic composition of the model were compared with those of the radiologists using the McNemar-Bowker test. Enrolled nodules were divided into 3-6-, 6-8-, and 8-10-mm subgroups. For each subgroup, the diagnostic results of the model were compared with those of the radiologists.

Results

The accuracy of the DL model, in differentiating malignant and benign SSPNs, was significantly higher than that of the radiologists (71.5% vs. 38.5%, P<0.001). The DL model reported more benign or malignant deterministic results and fewer indeterminate results. In subgroup analysis of nodule size, the DL model also yielded higher performance in comparison with that of the radiologists, providing fewer indeterminate results. The accuracy of the two methods in the 3-6-, 6-8-, and 8-10-mm subgroups was 75.5% vs. 28.3% (P<0.001), 62.0% vs. 28.2% (P<0.001), and 77.6% vs. 55.3% (P=0.001), respectively, and the indeterminate results were 3.8% vs. 66.0%, 8.5% vs. 66.2%, and 2.6% vs. 35.5% (all P<0.001), respectively.

Conclusions

The DL-based method yielded higher performance in comparison with that of the radiologists in differentiating malignant and benign SSPNs. This DL model may reduce uncertainty in diagnosis and improve diagnostic accuracy, especially for SSPNs smaller than 8 mm.

A Synthesizing Semantic Characteristics Lung Nodules Classification Method Based on 3D Convolutional Neural Network

Early detection is crucial for the survival and recovery of lung cancer patients. Computer-aided diagnosis system can assist in the early diagnosis of lung cancer by providing decision support. While deep learning methods are increasingly being applied to tasks such as CAD (Computer-aided diagnosis system), these models lack interpretability. In this paper, we propose a convolutional neural network model that combines semantic characteristics (SCCNN) to predict whether a given pulmonary nodule is malignant. The model synthesizes the advantages of multi-view, multi-task and attention modules in order to fully simulate the actual diagnostic process of radiologists. The 3D (three dimensional) multi-view samples of lung nodules are extracted by spatial sampling method. Meanwhile, semantic characteristics commonly used in radiology reports are used as an auxiliary task and serve to explain how the model interprets. The introduction of the attention module in the feature fusion stage improves the classification of lung nodules as benign or malignant. Our experimental results using the LIDC-IDRI (Lung Image Database Consortium and Image Database Resource Initiative) show that this study achieves 95.45% accuracy and 97.26% ROC (Receiver Operating Characteristic) curve area. The results show that the method we proposed not only realize the classification of benign and malignant compared to standard 3D CNN approaches but can also be used to intuitively explain how the model makes predictions, which can assist clinical diagnosis.

Computed Tomography and 18F-Fluorodeoxyglucose-Positron Emission Tomography/Computed Tomography Imaging Biomarkers of Lung Invasive Non-mucinous Adenocarcinoma: Prediction of Grade 3 Tumour Based on World Health Organization Grading System

AIMS

To evaluate computed tomography (CT) and 18F-fluorodeoxyglucose-positron emission tomography/computed tomography (18F-FDG-PET/CT) findings of invasive non-mucinous adenocarcinoma (INMA) of the lung as a predictor of histological tumour grade according to 2021 World Health Organization (WHO) classification.

MATERIALS AND METHODS

This retrospective study included consecutive patients with surgically resected INMA who underwent both preoperative CT and 18F-FDG-PET/CT. A three-tiered tumour grade was performed based on the fifth edition of the WHO classification of lung tumours. CT imaging features and the maximum standardised uptake value (SUVmax) were compared among the three tumour grades.

RESULTS

In total, 214 patients with INMA (median age 70 years; interquartile range 65-76 years; 123 men) were histologically categorised: 36 (17%) as grade 1, 102 (48%) as grade 2 and 76 (35%) as grade 3. Pure solid appearance was more frequent in grade 3 (83%) than in grades 1 (0%) and 2 (26%) (P < 0.001). The SUVmax of the entire tumour was higher in grade 3 than in grades 1 and 2 (P < 0.001). Multivariable analysis revealed that pure solid appearance (odds ratio = 94.0; P < 0.001), round/oval shape (odds ratio = 4.01; P = 0.001), spiculation (odds ratio = 2.13; P = 0.04), air bronchogram (odds ratio = 0.40; P = 0.03) and SUVmax (odds ratio = 1.45; P < 0.001) were significant predictors for grade 3 INMAs.

CONCLUSION

Pure solid appearance, round/oval shape, spiculation, absence of air bronchogram and high SUVmax were associated with grade 3 INMAs. CT and 18F-FDG-PET/CT were potentially useful non-invasive imaging methods to predict the histological grade of INMAs.

Correlations between iodine uptake, invasive CT features and pleural invasion in adenocarcinomas with pleural contact

Pleural contact in lung cancers does not always imply pleural invasion (PI). This study was designed to determine whether specific invasive CT characteristics or iodine uptake can aid in the prediction of PI. The sample population comprised patients with resected solid lung adenocarcinomas between April 2019 and May 2022. All participants underwent a contrast enhanced spectral CT scan. Two proficient radiologists independently evaluated the CT features and iodine uptake. Logistic regression analyses were employed to identify predictors for PI, via CT features and iodine uptake. To validate the improved diagnostic efficiency, accuracy analysis and ROC curves were subsequently used. A two-tailed P value of less than 0.05 was considered statistically significant. We enrolled 97 consecutive patients (mean age, 61.8 years ± 10; 48 females) in our study. The binomial logistic regression model revealed that a contact length > 10 mm (OR 4.80, 95% CI 1.92, 11.99, p = 0.001), and spiculation sign (OR 2.71, 95% CI 1.08, 6.79, p = 0.033) were independent predictors of PI, while iodine uptake was not. Enhanced sensitivity (90%) and a greater area under the curve (0.73) were achieved by integrating the two aforementioned CT features in predicting PI. We concluded that the combination of contact length > 10 mm and spiculation sign can enhance the diagnostic performance of PI.

Differentiation of pulmonary solid nodules attached to the pleura detected by thin-section CT

BACKGROUND

Pulmonary solid pleura-attached nodules (SPANs) are not very commonly detected and thus not well studied and understood. This study aimed to identify the clinical and CT characteristics for differentiating benign and malignant SPANs.

RESULTS

From January 2017 to March 2023, a total of 295 patients with 300 SPANs (128 benign and 172 malignant) were retrospectively enrolled. Between benign and malignant SPANs, there were significant differences in patients' age, smoking history, clinical symptoms, CT features, nodule-pleura interface, adjacent pleural change, peripheral concomitant lesions, and lymph node enlargement. Multivariate analysis revealed that smoking history (odds ratio [OR], 2.016; 95% confidence interval [CI], 1.037-3.919; p = 0.039), abutting the mediastinal pleura (OR, 3.325; 95% CI, 1.235-8.949; p = 0.017), nodule diameter (> 15.6 mm) (OR, 2.266; 95% CI, 1.161-4.423; p = 0.016), lobulation (OR, 8.922; 95% CI, 4.567-17.431; p < 0.001), narrow basement to pleura (OR, 6.035; 95% CI, 2.847-12.795; p < 0.001), and simultaneous hilar and mediastinal lymph nodule enlargement (OR, 4.971; 95% CI, 1.526-16.198; p = 0.008) were independent predictors of malignant SPANs, and the area under the curve (AUC) of this model was 0.890 (sensitivity, 82.0%, specificity, 77.3%) (p < 0.001).

CONCLUSION

In patients with a smoking history, SPANs abutting the mediastinal pleura, having larger size (> 15.6 mm in diameter), lobulation, narrow basement, or simultaneous hilar and mediastinal lymph nodule enlargement are more likely to be malignant.

CRITICAL RELEVANCE STATEMENT

The benign and malignant SPANs have significant differences in clinical and CT features. Understanding the differences between benign and malignant SPANs is helpful for selecting the high-risk ones and avoiding unnecessary surgical resection.

KEY POINTS

• The solid pleura-attached nodules (SPANs) are closely related to the pleura. • Relationship between nodule and pleura and pleural changes are important for differentiating SPANs. • Benign SPANs frequently have broad pleural thickening or embed in thickened pleura. • Smoking history and lesions abutting the mediastinal pleura are indicators of malignant SPANs. • Malignant SPANs usually have larger diameters, lobulation signs, narrow basements, and lymphadenopathy.

Significance of marginal vessels in differentiating peripheral small-cell lung cancer and benign lung tumor

BACKGROUND

Some peripheral small cell lung cancers (pSCLCs) and benign lung tumors (pBLTs) have similar morphological features but different treatment and prognosis.

PURPOSE

To determine the significance of marginal vessels in differentiating pSCLCs and pBLTs.

MATERIAL AND METHODS

A total of 57 and 95 patients with pathological confirmed nodular (≤3 cm) pSCLC and pBLT with similar morphological features were enrolled in this study retrospectively. The patients' clinical characteristics and computed tomography (CT) features of tumors and marginal vessels (vessels connecting with tumors) were analyzed and compared.

RESULTS

Compared with pBLTs, pSCLCs had a larger diameter (P = 0.001) but lower enhancement (P = 0.015) and fewer had calcification (P = 0.013). Compared with pBLTs, more lesions had proximal (70.2% vs. 22.1%) and distal (59.6% vs. 4.2%) marginal vessels in pSCLCs (each P < 0.0001). In addition, in pSCLCs, the numbers of proximal (1.3 ± 1.4 vs. 0.3 ± 0.6), distal (2.4 ± 3.1 vs. 0.1 ± 0.5), and total (3.6 ± 3.5 vs. 0.4 ± 1.0) marginal vessels were all more than those in pBLTs (each P < 0.001). Receiver operating characteristic curve analysis revealed the positive distal marginal vessel sign had the highest specificity (95.8%), and the number of total marginal vessels had the best performance in discriminating pSCLC from pBLT (cutoff value = 1.5, AUC = 0.80, 95% CI = 0.72-0.89, sensitivity = 70.2%, and specificity = 91.6%).

CONCLUSION

For peripheral solid nodules similar to pBLTs but without any calcification, the possibility of pSCLC should be considered if they have multiple marginal vessels (≥2), especially the distal ones.

Differentiating primary from secondary lung cancer with FDG PET/CT and extra-pulmonary tumor grade

OBJECTIVE

Assess feasibility of differentiating primary from secondary lung cancer in patients with a solid solitary malignant pulmonary lesion (SMPL) and a previously resected extrapulmonary tumor.

METHODS

Patients with pathology proven primary or secondary lung cancer from a solitary pulmonary lesion and known histopathology of extrapulmonary tumor were included. Patients with a small pulmonary lesion size, multiple malignant pulmonary nodules or an active infectious/inflammatory process were excluded. Extrapulmonary tumor grade was categorized as low, intermediate and high and was matched to FDG uptake intensity of SMPL, with FDG uptake range (SMPL/Liver SUVmax) of <0.9 for low, 0.91-1.99 for intermediate and >2.0 for high extrapulmonary tumor grade.

RESULTS

Of 274 patients, 62 met the study criteria. 46 are primary and 16 are secondary lung cancer. There are 19 low, 27 intermediate and 16 high grade extrapulmonary tumors. Mean SMPL SUVmax is 8.2 ± 4.5 and SMPL/liver SUVmax is 2.4 ± 1.4. There are 37 cases (60%) with mismatched results (e.g., low FDG SMPL with intermediate or high grade extrapulmonary tumor or vice versa) and 25 matched cases (40%) that are inconclusive (e.g., low FDG with low tumor grade or high FDG with high tumor grade). Of the mismatched cases, we correctly predicted 30 cases (81%) as primary lung cancers.

CONCLUSION

A mismatch between the SMPL SUVmax and the extrapulmonary tumor grade could be used to differentiate a primary lung cancer from a metastasis with reasonable accuracy. Our preliminary results support the hypothesis that FDG uptake intensity of a metastatic pulmonary lesion mirrors the tumor aggressiveness of its extrapulmonary neoplasm of origin.

Radiomics and artificial intelligence for precision medicine in lung cancer treatment

Lung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human eye but can be captured non-invasively on medical imaging as radiomic features, which can form a high dimensional data space amenable to machine learning. Radiomic features can be harnessed and used in an artificial intelligence paradigm to risk stratify patients, and predict for histological and molecular findings, and clinical outcome measures, thereby facilitating precision medicine for improving patient care. Compared to tissue sampling-driven approaches, radiomics-based methods are superior for being non-invasive, reproducible, cheaper, and less susceptible to intra-tumoral heterogeneity. This review focuses on the application of radiomics, combined with artificial intelligence, for delivering precision medicine in lung cancer treatment, with discussion centered on pioneering and groundbreaking works, and future research directions in the area.

Apical pulmonary lesions suspected of malignancy visible on neck CT angiography performed for acute stroke: Prevalence, treatment, and clinical implications - the PLEURA study

BACKGROUND

Computed tomography angiography (CTA) of the supraaortic arteries is commonly used for acute stroke workup and may reveal apical pulmonary lesions (APL).

AIM

To determine the prevalence, follow-up algorithms, and in-hospital outcomes of stroke patients with APL on CTA.

METHODS

We retrospectively included consecutive adult patients with ischemic stroke, transient ischemic attack, or intracerebral hemorrhage and available CTA at a tertiary hospital between January 2014 and May 2021. We reviewed all CTA reports for the presence of APL. APL were classified as malignancy suspicious or benign appearing based on radiological-morphological criteria. We performed regression analyses to investigate the impact of malignancy suspicious APL on different in-hospital outcome parameters.

RESULTS

Among 2715 patients, APL on CTA were found in 161 patients (5.9% [95%CI: 5.1-6.9]; 161/2715). Suspicion of malignancy was present in one third of patients with APL (36.0% [95%CI: 29.0-43.7]; 58/161), 42 of whom (72.4% [95%CI: 60.0-82.2]; 42/58) had no history of lung cancer or metastases. When performed, further investigations confirmed primary or secondary pulmonary malignancy in three-quarters (75.0% [95%CI: 50.5-89.8]; 12/16), with two patients (16.7% [95%CI: 4.7-44.8]; 2/12) receiving de novo oncologic therapy. In multivariable regression, the presence of radiologically malignancy suspicious APL was associated with higher NIHSS scores at 24 h (beta = 0.67, 95%CI: 0.28-1.06, p = 0.001) and all-cause in-hospital mortality (aOR = 3.83, 95%CI: 1.29-9.94, p = 0.01).

CONCLUSIONS

One in seventeen patients shows APL on CTA, of which one-third is malignancy suspicious. Further work-up confirmed pulmonary malignancy in a substantial number of patients triggering potentially life-saving oncologic therapy.

Value of dual-layer spectral detector computed tomography in the diagnosis of benign/malignant solid solitary pulmonary nodules and establishment of a prediction model

Objective

This study aimed to investigate the role of spectral detector computed tomography (SDCT) quantitative parameters and their derived quantitative parameters combined with lesion morphological information in the differential diagnosis of solid SPNs.

Methods

This retrospective study included basic clinical data and SDCT images of 132 patients with pathologically confirmed SPNs (102 and 30 patients in the malignant and benign groups, respectively). The morphological signs of SPNs were evaluated and the region of interest (ROI) was delineated from the lesion to extract and calculate the relevant SDCT quantitative parameters, and standardise the process. Differences in qualitative and quantitative parameters between the groups were statistically analysed. A receiver operating characteristic (ROC) curve was constructed to evaluate the efficacy of the corresponding parameters in the diagnosis of benign and malignant SPNs. Statistically significant clinical data, CT signs and SDCT quantitative parameters were analysed using multivariate logistic regression to determine the independent risk factors for predicting benign and malignant SPNs, and the best multi-parameter regression model was established. Inter-observer repeatability was assessed using the intraclass correlation coefficient (ICC) and Bland-Altman plots.

Results

Malignant SPNs differed from benign SPNs in terms of size, lesion morphology, short spicule sign, and vascular enrichment sign (P< 0.05). The SDCT quantitative parameters and their derived quantitative parameters of malignant SPNs (SAR40keV, SAR70keV, Δ40keV, Δ70keV, CER40keV, CER70keV, NEF40keV, NEF70keV, λ, NIC, NZeff) were significantly higher than those of benign SPNs (P< 0.05). In the subgroup analysis, most parameters could distinguish between benign and adenocarcinoma groups (SAR40keV, SAR70keV, Δ40keV, Δ70keV, CER40keV, CER70keV, NEF40keV, NEF70keV, λ, NIC, and NZeff), and between benign and squamous cell carcinoma groups (SAR40keV, SAR70keV, Δ40keV, Δ70keV, NEF40keV, NEF70keV, λ, and NIC). However, there were no significant differences between the parameters in the adenocarcinoma and squamous cell carcinoma groups. ROC curve analysis indicated that NIC, NEF70keV, and NEF40keV had higher diagnostic efficacy for differentiating benign and malignant SPNs (area under the curve [AUC]:0.869, 0.854, and 0.853, respectively), and NIC was the highest. Multivariate logistic regression analysis showed that size (OR=1.138, 95% CI 1.022-1.267, P=0.019), Δ70keV (OR=1.060, 95% CI 1.002-1.122, P=0.043), and NIC (OR=7.758, 95% CI 1.966-30.612, P=0.003) were independent risk factors for the prediction of benign and malignant SPNs. ROC curve analysis showed that the AUC of size, Δ70keV, NIC, and a combination of the three for differential diagnosis of benign and malignant SPNs were 0.636, 0.846, 0.869, and 0.903, respectively. The AUC for the combined parameters was the largest, and the sensitivity, specificity, and accuracy were 88.2%, 83.3% and 86.4%, respectively. The SDCT quantitative parameters and their derived quantitative parameters in this study exhibited satisfactory inter-observer repeatability (ICC: 0.811-0.997).

Conclusion

SDCT quantitative parameters and their derivatives can be helpful in the differential diagnosis of benign and malignant solid SPNs. The quantitative parameter, NIC, is superior to the other relevant quantitative parameters and when NIC is combined with lesion size and Δ70keV value for comprehensive diagnosis, the efficacy could be further improved.

Metabolomic differentiation of benign vs malignant pulmonary nodules with high specificity via high-resolution mass spectrometry analysis of patient sera

Differential diagnosis of pulmonary nodules detected by computed tomography (CT) remains a challenge in clinical practice. Here, we characterize the global metabolomes of 480 serum samples including healthy controls, benign pulmonary nodules, and stage I lung adenocarcinoma. The adenocarcinoma demonstrates a distinct metabolomic signature, whereas benign nodules and healthy controls share major similarities in metabolomic profiles. A panel of 27 metabolites is identified in the discovery cohort (n = 306) to distinguish between benign and malignant nodules. The discriminant model achieves an AUC of 0.915 and 0.945 in the internal validation (n = 104) and external validation cohort (n = 111), respectively. Pathway analysis reveals elevation in glycolytic metabolites associated with decreased tryptophan in serum of lung adenocarcinoma vs benign nodules and healthy controls, and demonstrates that uptake of tryptophan promotes glycolysis in lung cancer cells. Our study highlights the value of the serum metabolite biomarkers in risk assessment of pulmonary nodules detected by CT screening.

CT imaging indications correlate with the degree of lung adenocarcinoma infiltration

Background

Ground glass nodules (GGN) of the lung may be a precursor of lung cancer and have received increasing attention in recent years with the popularity of low-dose high-resolution computed tomography (CT). Many studies have discussed imaging features that suggest the benignity or malignancy of GGN, but the extent of its postoperative pathological infiltration is poorly understood. In this study, we identified CT imaging features that indicate the extent of GGN pathological infiltration.

Methods

A retrospective analysis of 189 patients with pulmonary GGN from January 2020 to December 2021 at Shanxi Cancer Hospital was performed. Patients were classified according to their pathological type into non-invasive adenocarcinoma [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS) in a total of 34 cases], micro-invasive adenocarcinoma (MIA) in 80 cases, and invasive adenocarcinoma (IAC) in a total of 75 cases. The general demographic data, nodule size, nodule area, solid component, CT indications and pathological findings of the three groups of patients were analyzed to predict the correlation between GGN and the degree of lung adenocarcinoma infiltration.

Results

No statistically significant differences were found among the three groups in general information, vascular signs, and vacuolar signs (P &gt; 0.05). Statistically significant differences among the three groups were found in nodule size, nodule area, lobar signs, pleural traction, burr signs, bronchial signs, and solid components (P &lt; 0.05). Logistic regression equation tests based on the statistically significant indicators showed that nodal area, lobar sign, pleural pull, burr sign, bronchial sign, and solid component were independent predictors of lung adenocarcinoma infiltration. The subject operating characteristic (ROC) curve analysis showed that nodal area is valuable in predicting GGN infiltration.

Conclusion

CT-based imaging indications are useful predictors of infiltrative adenocarcinoma manifested as pulmonary ground glass nodules.

A comprehensive nomogram combining CT-based radiomics with clinical features for differentiation of benign and malignant lung subcentimeter solid nodules

Objective

To establish a nomogram based on non-enhanced computed tomography(CT) imaging radiomics and clinical features for use in predicting the malignancy of sub-centimeter solid nodules (SCSNs).

Materials and methods

Retrospective analysis was performed of records for 198 patients with SCSNs that were surgically resected and examined pathologically at two medical institutions between January 2020 and June 2021. Patients from Center 1 were included in the training cohort (n = 147), and patients from Center 2 were included in the external validation cohort (n = 52). Radiomic features were extracted from chest CT images. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomic feature extraction and computation of radiomic scores. Clinical features, subjective CT findings, and radiomic scores were used to build multiple predictive models. Model performance was examined by evaluating the area under the receiver operating characteristic curve (AUC). The best model was selected for efficacy evaluation in a validation cohort, and column line plots were created.

Results

Pulmonary malignant nodules were significantly associated with vascular alterations in both the training (p &lt; 0.001) and external validation (p &lt; 0.001) cohorts. Eleven radiomic features were selected after a dimensionality reduction to calculate the radiomic scores. Based on these findings, three prediction models were constructed: subjective model (Model 1), radiomic score model (Model 2), and comprehensive model (Model 3), with AUCs of 0.672, 0.888, and 0.930, respectively. The optimal model with an AUC of 0.905 was applied to the validation cohort, and decision curve analysis indicated that the comprehensive model column line plot was clinically useful.

Conclusion

Predictive models constructed based on CT-based radiomics with clinical features can help clinicians diagnose pulmonary nodules and guide clinical decision making.

Multimodality CT imaging contributes to improving the diagnostic accuracy of solitary pulmonary nodules: a multi-institutional and prospective study

BACKGROUND

Solitary pulmonary nodules (SPNs) are one of the most common chest computed tomography (CT) abnormalities clinically. We aimed to investigate the value of non-contrast enhanced CT (NECT), contrast enhanced CT (CECT), CT perfusion imaging (CTPI), and dual- energy CT (DECT) used for differentiating benign and malignant SPNs with a multi-institutional and prospective study.

PATIENTS AND METHODS

Patients with 285 SPNs were scanned with NECT, CECT, CTPI and DECT. Differences between the benign and malignant SPNs on NECT, CECT, CTPI, and DECT used separately (NECT combined with CECT, DECT, and CTPI were methods of A, B, and C) or in combination (Method A + B, A + C, B + C, and A + B + C) were compared by receiver operating characteristic curve analysis.

RESULTS

Multimodality CT imaging showed higher performances (sensitivities of 92.81% to 97.60%, specificities of 74.58% to 88.14%, and accuracies of 86.32% to 93.68%) than those of single modality CT imaging (sensitivities of 83.23% to 85.63%, specificities of 63.56% to 67.80%, and accuracies of 75.09% to 78.25%, all p < 0.05).

CONCLUSIONS

SPNs evaluated with multimodality CT imaging contributes to improving the diagnostic accuracy of benign and malignant SPNs. NECT helps to locate and evaluate the morphological characteristics of SPNs. CECT helps to evaluate the vascularity of SPNs. CTPI using parameter of permeability surface and DECT using parameter of normalized iodine concentration at the venous phase both are helpful for improving the diagnostic performance.

Immunocyte count combined with CT features for distinguishing pulmonary tuberculoma from malignancy among non-calcified solitary pulmonary solid nodules

Background

Tuberculoma is the most common type of surgically removed benign solid solitary pulmonary nodule (SPN) and can lead to a high risk of misdiagnoses for clinicians. This study aimed to discuss the value of the immunocyte count combined with computed tomography (CT) features in distinguishing pulmonary tuberculoma from malignancy among non-calcified solid SPNs.

Methods

Forty-eight patients with pulmonary tuberculoma and 52 patients with lung cancer were retrospectively included in our study. Univariate and multivariate analyses were conducted to screen the independent predictors. Receiver operating characteristic (ROC) analysis was performed to investigate the validity of the predictive model.

Results

The univariate and multivariate analyses revealed that a coarse margin, vacuole, lobulation, pleural indentation, cluster of differentiation (CD)3⁺ T-lymphocyte count, and CD4⁺ T-lymphocyte count were independent predictors for distinguishing pulmonary tuberculoma from malignancy. The sensitivity, specificity, accuracy, and the area under the ROC curve of the model comprising the CD3⁺ T-lymphocyte count were 79.2%, 75%, 74.5%, and 0.845 [95% confidence interval (CI), 0.759-0.910], respectively, and those of the model involving the CD4⁺ T-lymphocyte count were 77.1%, 78.8%, 77.1%, and 0.857 (95% CI, 0.773-0.919), respectively.

Conclusions

Immunocyte count combined with CT features is efficient in distinguishing pulmonary tuberculoma from malignancy among non-calcified solid SPNs and has applicable clinical value.

Minimally invasive interventions for biopsy of malignancy-suspected pulmonary nodules: a systematic review and meta-analysis

BACKGROUND

Imaging tests are important for diagnosis during the management of pulmonary nodules; however, biopsy is required to confirm the malignancy.

OBJECTIVES

To compare the effects of different techniques used for the biopsy of a pulmonary nodule.

DESIGN AND SETTING

Systematic review and meta-analysis were conducted using Cochrane methodology in São Paulo, São Paulo, Brazil.

METHODS

We conducted a systematic review of randomized controlled trials (RCTs) on minimally invasive techniques, including tomography-guided percutaneous biopsy (PERCUT), transbronchial biopsies with fluoroscopy (FLUOR), endobronchial ultrasound (EBUSR), and electromagnetic navigation (NAVIG). The primary outcomes were diagnostic yield, major adverse events, and need for another approach.

RESULTS

Seven RCTs were included (913 participants; 39.2% female, mean age: 59.28 years). Little to no increase was observed in PERCUT over FLUOR (P = 0.84), PERCUT over EBUSR (P = 0.32), and EBUSR over NAVIG (P = 0.17), whereas a slight increase was observed in NAVIG over FLUOR (P = 0.17); however, the evidence was uncertain. EBUSR may increase the diagnostic yield over FLUOR (P = 0.34). PERCUT showed little to no increase in all bronchoscopic techniques, with uncertain evidence (P = 0.02).

CONCLUSION

No biopsy method is definitively superior to others. The preferred approach must consider availability, accessibility, and cost, as safety and diagnostic yield do not differ. Further RCTs planned, conducted, and reported with methodological rigor and transparency are needed, and additional studies should assess cost and the correlation between nodule size and location, as well as their association with biopsy results.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO database, CRD42018092367 -https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=92367.

Derivation and validation of a prediction model for patients with lung nodules malignancy regardless of mediastinal/hilar lymphadenopathy

BACKGROUND

Clinical prediction models to classify lung nodules often exclude patients with mediastinal/hilar lymphadenopathy, although the presence of mediastinal/hilar lymphadenopathy does not always indicate malignancy. Herein, we developed and validated a multimodal prediction model for lung nodules in which patients with mediastinal/hilar lymphadenopathy were included.

METHODS

A single-center retrospective study was conducted. We developed and validated a logistic regression model including patients with mediastinal/hilar lymphadenopathy. Discrimination of the model was assessed by area under the operating curve. Goodness of fit test was performed via the Hosmer-Lemeshow test, and a nomogram of the logistic regression model was drawn.

RESULTS

There were 311 cases included in the final analysis. A logistic regression model was developed and validated. There were nine independent variables included in the model. The aera under the curve (AUC) of the validation set was 0.91 (95% confidence interval [CI]: 0.85-0.98). In the validation set with mediastinal/hilar lymphadenopathy, the AUC was 0.95 (95% CI: 0.90-0.99). The goodness-of-fit test was 0.22.

CONCLUSIONS

We developed and validated a multimodal risk prediction model for lung nodules with excellent discrimination and calibration, regardless of mediastinal/hilar lymphadenopathy. This broadens the application of lung nodule prediction models. Furthermore, mediastinal/hilar lymphadenopathy added value for predicting lung nodule malignancy in clinical practice.

The diagnostic value of magnetic resonance imaging compared to computed tomography in the evaluation of fat-containing thoracic lesions

Intrathoracic fat-containing lesions may arise in the mediastinum, lungs, pleura, or chest wall. While CT can be helpful in the detection and diagnosis of these lesions, it can only do so if the lesions contain macroscopic fat. Furthermore, because CT cannot demonstrate microscopic or intravoxel fat, it can fail to identify and diagnose microscopic fat-containing lesions. MRI, employing spectral and chemical shift fat suppression techniques, can identify both macroscopic and microscopic fat, with resultant enhanced capability to diagnose these intrathoracic lesions non-invasively and without ionizing radiation. This paper aims to review the CT and MRI findings of fat-containing lesions of the chest and describes the fat-suppression techniques utilized in their assessment.

Differential diagnosis of benign and malignant patchy ground-glass opacity by thin-section computed tomography

Background

Previous studies confirmed that ground-glass nodules (GGNs) with certain CT manifestations had a higher probability of malignancy. However, differentiating patchy ground-glass opacities (GGOs) and GGNs has not been discussed solely. This study aimed to investigate the differences between the CT features of benign and malignant patchy GGOs to improve the differential diagnosis.

Methods

From January 2016 to September 2021, 226 patients with 247 patchy GGOs (103 benign and 144 malignant) confirmed by postoperative pathological examination or follow-up were retrospectively enrolled. Their clinical and CT data were reviewed, and their CT features were compared. A binary logistic regression analysis was performed to reveal the predictors of malignancy.

Results

Compared to patients with benign patchy GGOs, malignant cases were older (P <  0.001), had a lower incidence of malignant tumor history (P = 0.003), and more commonly occurred in females (P = 0.012). Based on CT images, there were significant differences in the location, distribution, density pattern, internal bronchial changes, and boundary between malignant and benign GGOs (P <  0.05). The binary logistic regression analysis revealed that the independent predictors of malignant GGOs were the following: patient age ≥ 58 years [odds ratio (OR), 2.175; 95% confidence interval (CI), 1.135–6.496; P = 0.025], locating in the upper lobe (OR, 5.481; 95%CI, 2.027–14.818; P = 0.001), distributing along the bronchovascular bundles (OR, 12.770; 95%CI, 4.062–40.145; P < 0.001), centrally distributed solid component (OR, 3.024; 95%CI, 1.124–8.133; P = 0.028), and well-defined boundary (OR, 5.094; 95%CI, 2.079–12.482; P < 0.001).

Conclusions

In older patients (≥58 years), well-defined patchy GGOs with centric solid component, locating in the upper lobe, and distributing along the bronchovascular bundles should be highly suspected as malignancy.

A Radiomics Approach on Chest CT Distinguishes Primary Lung Cancer from Solitary Lung Metastasis in Colorectal Cancer Patients

PURPOSE

This study utilized a radiomics approach combined with a machine learning algorithm to distinguish primary lung cancer (LC) from solitary lung metastasis (LM) in colorectal cancer (CRC) patients with a solitary pulmonary nodule (SPN).

MATERIALS AND METHODS

In a retrospective study, 239 patients who underwent chest computerized tomography (CT) at three different institutions between 2011 and 2019 and were diagnosed as primary LC or solitary LM were included. The data from the first institution were divided into training and internal testing datasets. The data from the second and third institutions were used as an external testing dataset. Radiomic features were extracted from the intra and perinodular regions of interest (ROI). After a feature selection process, Support vector machine (SVM) was used to train models for classifying between LC and LM. The performances of the SVM classifiers were evaluated with both the internal and external testing datasets. The performances of the model were compared to those of two radiologists who reviewed the CT images of the testing datasets for the binary prediction of LC versus LM.

RESULTS

The SVM classifier trained with the radiomic features from the intranodular ROI and achieved the sensitivity/specificity of 0.545/0.828 in the internal test dataset, and 0.833/0.964 in the external test dataset, respectively. The SVM classifier trained with the combined radiomic features from the intra- and perinodular ROIs achieved the sensitivity/specificity of 0.545/0.966 in the internal test dataset, and 0.833/1.000 in the external test data set, respectively. Two radiologists demonstrated the sensitivity/specificity of 0.545/0.966 and 0.636/0.828 in the internal test dataset, and 0.917/0.929 and 0.833/0.929 in the external test dataset, which were comparable to the performance of the model trained with the combined radiomics features.

CONCLUSION

Our results suggested that the machine learning classifiers trained using radiomics features of SPN in CRC patients can be used to distinguish the primary LC and the solitary LM with a similar level of performance to radiologists.

An Effective Method for Lung Cancer Diagnosis from CT Scan Using Deep Learning-Based Support Vector Network

The diagnosis of early-stage lung cancer is challenging due to its asymptomatic nature, especially given the repeated radiation exposure and high cost of computed tomography(CT). Examining the lung CT images to detect pulmonary nodules, especially the cell lung cancer lesions, is also tedious and prone to errors even by a specialist. This study proposes a cancer diagnostic model based on a deep learning-enabled support vector machine (SVM). The proposed computer-aided design (CAD) model identifies the physiological and pathological changes in the soft tissues of the cross-section in lung cancer lesions. The model is first trained to recognize lung cancer by measuring and comparing the selected profile values in CT images obtained from patients and control patients at their diagnosis. Then, the model is tested and validated using the CT scans of both patients and control patients that are not shown in the training phase. The study investigates 888 annotated CT scans from the publicly available LIDC/IDRI database. The proposed deep learning-assisted SVM-based model yields 94% accuracy for pulmonary nodule detection representing early-stage lung cancer. It is found superior to other existing methods including complex deep learning, simple machine learning, and the hybrid techniques used on lung CT images for nodule detection. Experimental results demonstrate that the proposed approach can greatly assist radiologists in detecting early lung cancer and facilitating the timely management of patients.