Radiological Features of Primary Pulmonary Invasive Mucinous Adenocarcinoma Based on 312 Consecutive Patients

BACKGROUND

The aim of this study is to investigate the radiological features of primary pulmonary invasive mucinous adenocarcinoma (IMA) in a relatively large population to help improve its further understanding and its accuracy of initial diagnosis.

METHODS

This retrospective study included consecutive patients with pathologically confirmed primary pulmonary IMA from January 2019 to December 2021. According to tumor morphology, IMAs were divided into regular nodule/mass, irregular, and large consolidative types. According to tumor density, IMAs were divided into solid, halo, part-solid, pure ground-glass, and cystic types. ANOVA, chi-square, or Fisher exact tests were used to analyze the differences in radiological and clinicopathological characteristics of IMA according to morphological and density subtypes.

RESULTS

We analyzed 312 patients. Pulmonary IMA tended to occur in the elderly, with a slightly higher number of women than men. IMA showed a predominance in the lower lobe and adjacent to pleura. IMA of regular nodule/mass, irregular, and large consolidative types accounted for 80.8% (252/312), 13.8% (43/312), and 5.4% (17/312), respectively. Solid, halo, part-solid, pure ground-glass, and cystic IMAs accounted for 55.8% (174/312), 28.2% (88/312), 11.2% (35/312), 1.3% (4/312), and 3.5% (11/312), respectively. The lobulated (76.9%), spiculated (63.5%), and air bronchogram (56.7%) signs were common in IMA. Dead branch sign (88.2%), angiogram sign (88.2%), and satellite nodules/skipping lesions (47.1%) were common in large-consolidative-type IMA. Kirsten rat sarcoma viral oncogene mutations were common (56.1%), whereas epidermal growth factor receptor mutations were relatively rare (2.3%).

CONCLUSIONS

Pulmonary IMA of regular nodule/mass type and solid type were the most common at the initial diagnosis. Detailed radiological features can aid in the differential diagnosis of IMA.

Approach to Pulmonary Nodules in Connective Tissue Disease

The assessment of pulmonary nodules is a common and often challenging clinical scenario. This evaluation becomes even more complex in patients with connective tissue diseases (CTDs), as a range of disease-related factors must also be taken into account. These diseases are characterized by immune-mediated chronic inflammation, leading to tissue damage, collagen deposition, and subsequent organ dysfunction. A thorough examination of nodule features in these patients is required, incorporating anatomic and functional information, along with patient demographics, clinical factors, and disease-specific knowledge. This integrated approach is vital for effective risk stratification and precise diagnosis. This review article addresses specific CTD-related factors that should be taken into account when evaluating pulmonary nodules in this patient group.

Nodule-CLIP: Lung nodule classification based on multi-modal contrastive learning

The latest developments in deep learning have demonstrated the importance of CT medical imaging for the classification of pulmonary nodules. However, challenges remain in fully leveraging the relevant medical annotations of pulmonary nodules and distinguishing between the benign and malignant labels of adjacent nodules. Therefore, this paper proposes the Nodule-CLIP model, which deeply mines the potential relationship between CT images, complex attributes of lung nodules, and benign and malignant attributes of lung nodules through a comparative learning method, and optimizes the model in the image feature extraction network by using its similarities and differences to improve its ability to distinguish similar lung nodules. Firstly, we segment the 3D lung nodule information by U-Net to reduce the interference caused by the background of lung nodules and focus on the lung nodule images. Secondly, the image features, class features, and complex attribute features are aligned by contrastive learning and loss function in Nodule-CLIP to achieve lung nodule image optimization and improve classification ability. A series of testing and ablation experiments were conducted on the public dataset LIDC-IDRI, and the final benign and malignant classification rate was 90.6%, and the recall rate was 92.81%. The experimental results show the advantages of this method in terms of lung nodule classification as well as interpretability.

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.

Differentiation of Hamartomas and Malignant Lung Tumors in Single-Phased Dual-Energy Computed Tomography

This study investigated the efficacy of single-phase dual-energy CT (DECT) in differentiating pulmonary hamartomas from malignant lung lesions using virtual non-contrast (VNC), iodine, and fat quantification. Forty-six patients with 47 pulmonary lesions (mean age: 65.2 ± 12.1 years; hamartomas-to-malignant lesions = 22:25; male: 67%) underwent portal venous DECT using histology, PET-CT and follow-up CTs as a reference. Quantitative parameters such as VNC, fat fraction, iodine density and CT mixed values were statistically analyzed. Significant differences were found in fat fractions (hamartomas: 48.9%; malignancies: 22.9%; p ≤ 0.0001) and VNC HU values (hamartomas: -20.5 HU; malignancies: 17.8 HU; p ≤ 0.0001), with hamartomas having higher fat content and lower VNC HU values than malignancies. CT mixed values also differed significantly (p ≤ 0.0001), but iodine density showed no significant differences. ROC analysis favored the fat fraction (AUC = 96.4%; sensitivity: 100%) over the VNC, CT mixed value and iodine density for differentiation. The study concludes that the DECT-based fat fraction is superior to the single-energy CT in differentiating between incidental pulmonary hamartomas and malignant lesions, while post-contrast iodine density is ineffective for differentiation.

Is chest imaging needed as part of pT1a renal cell carcinoma surveillance after surgical resection?

INTRODUCTION

Following surgical excision of pT1a renal cell carcinoma (RCC), 2% to 5% will recur, with 50% to 60% being lung metastases. The ideal surveillance strategy to identify recurrences is unclear. Guidelines are mixed, with NCCN and AUA recommending surveillance via chest x-ray (CXR) at least annually for 5 years, while EAU guidelines do not specifically recommend the use of CXR. In an effort to clarify the utility of surveillance CXR, we retrospectively evaluated pT1a patients following surgical treatment at a single institution.

METHODS

We performed retrospective analysis of unique patients who underwent surgical excision of pT1 RCC between January 2000 and January 2020. In addition to demographic information, we collected RCC pathology, recurrence details, and most recent chest imaging. We excluded non-RCC pathology, and patients with pulmonary nodules on baseline imaging.

RESULTS

We identified 463 unique patients (mean age 58.3 years, range 23-87) that underwent surgical excision of pT1a RCC with mean follow-up of 47.6 months (range 1-201). On the most recent pulmonary surveillance imaging, 72.4% (335/463) had CXR while 27.6% (128/463) had chest CT performed. Regardless of modality, pulmonary recurrence was not detected on any surveillance imaging (0/463).

CONCLUSION

In patients without baseline preoperative lung pathology, we found that there is questionable clinical value in surveillance for pulmonary recurrence after resection of pT1a RCC.

Radiomics as a non-invasive adjunct to Chest CT in distinguishing benign and malignant lung nodules

In an observational study conducted from 2016 to 2021, we assessed the utility of radiomics in differentiating between benign and malignant lung nodules detected on computed tomography (CT) scans. Patients in whom a final diagnosis regarding the lung nodules was available according to histopathology and/or 2017 Fleischner Society guidelines were included. The radiomics workflow included lesion segmentation, region of interest (ROI) definition, pre-processing, and feature extraction. Employing random forest feature selection, we identified ten important radiomic features for distinguishing between benign and malignant nodules. Among the classifiers tested, the Decision Tree model demonstrated superior performance, achieving 79% accuracy, 75% sensitivity, 85% specificity, 82% precision, and 90% F1 score. The implementation of the XGBoost algorithm further enhanced these results, yielding 89% accuracy, 89% sensitivity, 89% precision, and an F1 score of 89%, alongside a specificity of 85%. Our findings highlight tumor texture as the primary predictor of malignancy, emphasizing the importance of texture-based features in computational oncology. Thus, our study establishes radiomics as a powerful, non-invasive adjunct to CT scans in the differentiation of lung nodules, with significant implications for clinical decision-making, especially for indeterminate nodules, and the enhancement of diagnostic and predictive accuracy in this clinical context.

ACR Appropriateness Criteria® Incidentally Detected Indeterminate Pulmonary Nodule

Incidental pulmonary nodules are common. Although the majority are benign, most are indeterminate for malignancy when first encountered making their management challenging. CT remains the primary imaging modality to first characterize and follow-up incidental lung nodules. This document reviews available literature on various imaging modalities and summarizes management of indeterminate pulmonary nodules detected incidentally. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

The Safety of CT-Guided Percutaneous Lung Biopsy in Elderly Patients With Solitary Pulmonary Nodules

OBJECTIVE

Computed tomography (CT)-guided percutaneous lung biopsy is an effective diagnostic procedure for patients with solitary pulmonary nodules (SPN). The aim of this study is to evaluate the safety of this procedure for elderly patients with SPN.

METHODS

A total of 125 patients with SPN who received a CT-guided percutaneous lung biopsy were retrospectively analyzed. Patients were divided into elderly (age 65 and above) and non-elderly groups. The patients' characteristics and procedure-related complications were compared between the two groups.

RESULTS

The elderly and non-elderly groups included 74 and 51 patients, respectively. The success rate of a CT-guided percutaneous lung biopsy was 100%. The diagnosis rate of lung cancer in the elderly group was significantly higher than that in the non-elderly group (83.78% vs. 64.70%, p = 0.014). The incidence of pulmonary hemorrhage after lung biopsy in the elderly group (44, 59.45%) was significantly higher than that in the non-elderly group (21, 41.17%, p = 0.044), and moderate hemorrhage was the main contributor. The incidence rate of pneumothorax in the elderly group numerically increased, but the difference did not reach statistical significance.

CONCLUSION

 Computed tomography-guided percutaneous lung biopsy was an efficient procedure for diagnosing SPN in elderly patients. Although complication rates were relatively higher in elderly patients, the safety of this procedure was acceptable.

Tips and Tricks in Thoracic Radiology for Beginners: A Findings-Based Approach

This review has the purpose of illustrating schematically and comprehensively the key concepts for the beginner who approaches chest radiology for the first time. The approach to thoracic imaging may be challenging for the beginner due to the wide spectrum of diseases, their overlap, and the complexity of radiological findings. The first step consists of the proper assessment of the basic imaging findings. This review is divided into three main districts (mediastinum, pleura, focal and diffuse diseases of the lung parenchyma): the main findings will be discussed in a clinical scenario. Radiological tips and tricks, and relative clinical background, will be provided to orient the beginner toward the differential diagnoses of the main thoracic diseases.

Diagnosis of primary lung cancer and benign pulmonary nodules: a comparison of the breath test and 18F-FDG PET-CT

With the application of low-dose computed tomography in lung cancer screening, pulmonary nodules have become increasingly detected. Accurate discrimination between primary lung cancer and benign nodules poses a significant clinical challenge. This study aimed to investigate the viability of exhaled breath as a diagnostic tool for pulmonary nodules and compare the breath test with 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)-computed tomography (CT). Exhaled breath was collected by Tedlar bags and analyzed by high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS). A retrospective cohort (n = 100) and a prospective cohort (n = 63) of patients with pulmonary nodules were established. In the validation cohort, the breath test achieved an area under the receiver operating characteristic curve (AUC) of 0.872 (95% CI 0.760-0.983) and a combination of 16 volatile organic compounds achieved an AUC of 0.744 (95% CI 0.7586-0.901). For PET-CT, the SUVmax alone had an AUC of 0.608 (95% CI 0.433-0.784) while after combining with CT image features, 18F-FDG PET-CT had an AUC of 0.821 (95% CI 0.662-0.979). Overall, the study demonstrated the efficacy of a breath test utilizing HPPI-TOFMS for discriminating lung cancer from benign pulmonary nodules. Furthermore, the accuracy achieved by the exhaled breath test was comparable with 18F-FDG PET-CT.

Deep learning predicts malignancy and metastasis of solid pulmonary nodules from CT scans

In the clinic, it is difficult to distinguish the malignancy and aggressiveness of solid pulmonary nodules (PNs). Incorrect assessments may lead to delayed diagnosis and an increased risk of complications. We developed and validated a deep learning-based model for the prediction of malignancy as well as local or distant metastasis in solid PNs based on CT images of primary lesions during initial diagnosis. In this study, we reviewed the data from multiple patients with solid PNs at our institution from 1 January 2019 to 30 April 2022. The patients were divided into three groups: benign, Ia-stage lung cancer, and T1-stage lung cancer with metastasis. Each cohort was further split into training and testing groups. The deep learning system predicted the malignancy and metastasis status of solid PNs based on CT images, and then we compared the malignancy prediction results among four different levels of clinicians. Experiments confirmed that human-computer collaboration can further enhance diagnostic accuracy. We made a held-out testing set of 134 cases, with 689 cases in total. Our convolutional neural network model reached an area under the ROC (AUC) of 80.37% for malignancy prediction and an AUC of 86.44% for metastasis prediction. In observer studies involving four clinicians, the proposed deep learning method outperformed a junior respiratory clinician and a 5-year respiratory clinician by considerable margins; it was on par with a senior respiratory clinician and was only slightly inferior to a senior radiologist. Our human-computer collaboration experiment showed that by simply adding binary human diagnosis into model prediction probabilities, model AUC scores improved to 81.80-88.70% when combined with three out of four clinicians. In summary, the deep learning method can accurately diagnose the malignancy of solid PNs, improve its performance when collaborating with human experts, predict local or distant metastasis in patients with T1-stage lung cancer, and facilitate the application of precision medicine.

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.

Multi-Label Softmax Networks for Pulmonary Nodule Classification Using Unbalanced and Dependent Categories

Radiographic attributes of lung nodules remedy the shortcomings of lung cancer computer-assisted diagnosis systems, which provides interpretable diagnostic reference for doctors. However, current studies fail to dedicate multi-label classification of lung nodules using convolutional neural networks (CNNs) and are inferior in exploiting statistical dependency between the labels. In addition, data imbalance is an indispensable problem to be reckoned with when employing CNNs to perform lung nodule classification. It introduces greater challenges especially in the multi-label classification. In this paper, we propose a method called MLSL-Net to discriminate lung nodule characteristics and simultaneously address the challenges. Particularly, the proposal employs multi-label softmax loss (MLSL) as the performance index, aiming to reduce the ranking errors between the labels and within the labels during training, thereby optimizing ranking loss and AUC directly. Such criterions can better evaluate the classifier's performance on the multi-label imbalanced dataset. Furthermore, a scale factor is introduced based on the investigation of the max surrogate function. Different from preceding usages, the small factor is used so that to narrow the discrepancy of gradients produced by different labels. More interestingly, this factor also facilitates the exploit of label dependency. Experimental results on the LIDC-IDRI dataset as well as another akin dataset demonstrate that MLSL-Net can effectively perform multi-label classification despite the imbalance issue. Meanwhile, the results confirm the responsibility of the factor for capturing label correlations, accordingly leading to more accurate predictions.

The value of chest computed tomography in evaluating lung cancer in a lobe affected by stable pulmonary tuberculosis in middle-aged and elderly patients: A preliminary study

Introduction

Lung cancer can be masked by coexisting stable tuberculosis lesions, which may result in delayed lung cancer diagnosis and treatment. Information about pulmonary tuberculosis patients who are at high-risk of developing lung cancer is scarce. We aimed to examine the value of chest computed tomography (CT) in evaluating lung cancer in a lobe affected by stable pulmonary tuberculosis in middle-aged and elderly patients.

Methods

In this single-centered, retrospective, observational study, we enrolled 41 middle-aged and elderly patients with pulmonary tuberculosis who developed lung cancer in the same lobe from January 30, 2011 to December 30, 2020. Comparisons of the clinical and chest CT data were made with age-matched and sex-matched control groups of patients with stable pulmonary tuberculosis but no lung cancer diagnosis (n = 38).

Results

Seventeen patients in the lung cancer group (41%) were initially misdiagnosed. Compared to lesions in the control group, lesions in the lung cancer group were significantly more likely to demonstrate the following CT features: large size, vessel convergence, lobulation, spiculation, spinous protuberance, bronchial obstruction or stenosis, vacuolation, ground-glass opacification, heterogeneous or homogeneous enhancement, and gradual increase in size. Nodular enlargement showed the best diagnostic performance in the diagnosis of lung cancer in a lobe affected by tuberculosis (area under the receiver operating characteristic curve = 0.974; P &lt;0.001; accuracy = 98.2%; sensitivity =94.7%; specificity = 100%).

Conclusion

Chest CT might play an important role in early diagnosis of lung cancer in a lobe affected by tuberculosis. Regular CT re-examination is necessary in continuous controls monitoring of patients with stable pulmonary tuberculosis. The study indicates necessity of prospective study in this field.

Establishment of a malignancy and benignancy prediction model of sub-centimeter pulmonary ground-glass nodules based on the inflammation-cancer transformation theory

Background

In recent years, Chinese clinicians are frequently encountered by patients with multiple lung nodules and these intensity ground-glass nodules (GGNs) are usually small in size and some of them have no spicule sign. In addition, early lung cancer is diagnosed in large numbers of non-heavy smokers and individuals with no caner history. Obviously, the Mayo model is not applicable to these patients. The aim of the present study is to develop a new and more applicable model that can predict malignancy or benignancy of pulmonary GGNs based on the inflammation-cancer transformation theory.

Materials and methods

Included in this study were patients who underwent surgical resection or lung puncture biopsy of GGNs in Shanghai 10th People’s Hospital between January 1, 2018 and May 31, 2021 with the inclusion criterion of the maximum diameter of GGN < 1.0 cm. All the included patients had their pulmonary GGNs diagnosed by postoperative pathology. The patient data were analyzed to establish a prediction model and the predictive value of the model was verified.

Results

Altogether 100 GGN patients who met the inclusion criteria were included for analysis. Based on the results of logistic stepwise regression analysis, a mathematical predication equation was established to calculate the malignancy probability as follows: Malignancy probability rate (p) = ex/(1 + ex); p > 0.5 was considered as malignant and p ≤ 0.5 as benign, where x = 0.9650 + [0.1791 × T helper (Th) cell] + [0.2921 × mixed GGN (mGGN)] + (0.4909 × vascular convergence sign) + (0.1058 × chronic inflammation). According to this prediction model, the positive prediction rate was 73.3% and the negative prediction rate was 100% versus the positive prediction rate of 0% for the Mayo model.

Conclusion

By focusing on four major factors (chronic inflammation history, human Th cell, imaging vascular convergence sign and mGGNs), the present prediction model greatly improves the accuracy of malignancy or benignancy prediction of sub-centimeter pulmonary GGNs. This is a breakthrough innovation in this field.

Identification of pulmonary adenocarcinoma and benign lesions in isolated solid lung nodules based on a nomogram of intranodal and perinodal CT radiomic features

To develop and validate a predictive model based on clinical radiology and radiomics to enhance the ability to distinguish between benign and malignant solitary solid pulmonary nodules. In this study, we retrospectively collected computed tomography (CT) images and clinical data of 286 patients with isolated solid pulmonary nodules diagnosed by surgical pathology, including 155 peripheral adenocarcinomas and 131 benign nodules. They were randomly divided into a training set and verification set at a 7:3 ratio, and 851 radiomic features were extracted from thin-layer enhanced venous phase CT images by outlining intranodal and perinodal regions of interest. We conducted preprocessing measures of image resampling and eigenvalue normalization. The minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (lasso) methods were used to downscale and select features. At the same time, univariate and multifactorial analyses were performed to screen clinical radiology features. Finally, we constructed a nomogram based on clinical radiology, intranodular, and perinodular radiomics features. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC), and the clinical decision curve (DCA) was used to evaluate the clinical practicability of the models. Univariate and multivariate analyses showed that the two clinical factors of sex and age were statistically significant. Lasso screened four intranodal and four perinodal radiomic features. The nomogram based on clinical radiology, intranodular, and perinodular radiomics features showed the best predictive performance (AUC=0.95, accuracy=0.89, sensitivity=0.83, specificity=0.96), which was superior to other independent models. A nomogram based on clinical radiology, intranodular, and perinodular radiomics features is helpful to improve the ability to predict benign and malignant solitary pulmonary nodules.

Form Factors as Potential Imaging Biomarkers to Differentiate Benign vs. Malignant Lung Lesions on CT Scans

Indeterminate lung nodules detected on CT scans are common findings in clinical practice. Their correct assessment is critical, as early diagnosis of malignancy is crucial to maximise the treatment outcome. In this work, we evaluated the role of form factors as imaging biomarkers to differentiate benign vs. malignant lung lesions on CT scans. We tested a total of three conventional imaging features, six form factors, and two shape features for significant differences between benign and malignant lung lesions on CT scans. The study population consisted of 192 lung nodules from two independent datasets, containing 109 (38 benign, 71 malignant) and 83 (42 benign, 41 malignant) lung lesions, respectively. The standard of reference was either histological evaluation or stability on radiological followup. The statistical significance was determined via the Mann-Whitney U nonparametric test, and the ability of the form factors to discriminate a benign vs. a malignant lesion was assessed through multivariate prediction models based on Support Vector Machines. The univariate analysis returned four form factors (Angelidakis compactness and flatness, Kong flatness, and maximum projection sphericity) that were significantly different between the benign and malignant group in both datasets. In particular, we found that the benign lesions were on average flatter than the malignant ones; conversely, the malignant ones were on average more compact (isotropic) than the benign ones. The multivariate prediction models showed that adding form factors to conventional imaging features improved the prediction accuracy by up to 14.5 pp. We conclude that form factors evaluated on lung nodules on CT scans can improve the differential diagnosis between benign and malignant lesions.

Cascaded-Recalibrated Multiple Instance Deep Model for Pathologic-Level Lung Cancer Prediction in CT Images

Lung cancer accounts for the greatest number of cancer-related mortality, while the accurate evaluation of pulmonary nodules in computed tomography (CT) images can significantly increase the 5-year relative survival rate. Despite deep learning methods that have recently been introduced to the identification of malignant nodules, a substantial challenge remains due to the limited datasets. In this study, we propose a cascaded-recalibrated multiple instance learning (MIL) model based on multiattribute features transfer for pathologic-level lung cancer prediction in CT images. This cascaded-recalibrated MIL deep model incorporates a cascaded recalibration mechanism at the nodule level and attribute level, which fuses the informative attribute features into nodule embeddings and then the key nodule features can be converged into the patient-level embedding to improve the performance of lung cancer prediction. We evaluated the proposed cascaded-recalibrated MIL model on the public Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) benchmark dataset and compared it to the latest approaches. The experimental results showed a significant performance boost by the cascaded-recalibrated MIL model over the higher-order transfer learning, instance-space MIL, and embedding-space MIL models and the radiologists. In addition, the recalibration coefficients of the nodule and attribute feature for the final decision were also analyzed to reveal the underlying relationship between the confirmed diagnosis and its highly-correlated attributes. The cascaded recalibration mechanism enables the MIL model to pay more attention to those important nodules and attributes while suppressing less-useful feature embeddings, and the cascaded-recalibrated MIL model provides substantial improvements for the pathologic-level lung cancer prediction by using the CT images. The identification of the important nodules and attributes also provides better interpretability for model decision-making, which is very important for medical applications.

[18F]FDG-PET/CT radiomics and artificial intelligence in lung cancer: Technical aspects and potential clinical applications

Lung cancer is the second most common cancer and the leading cause of cancer-related death worldwide. Molecular imaging using [¹⁸F]fluorodeoxyglucose Positron Emission Tomography and/or Computed Tomography ([¹⁸F]FDG-PET/CT) plays an essential role in the diagnosis, evaluation of response to treatment, and prediction of outcomes. The images are evaluated using qualitative and conventional quantitative indices. However, there is far more information embedded in the images, which can be extracted by sophisticated algorithms. Recently, the concept of uncovering and analyzing the invisible data extracted from medical images, called radiomics, is gaining more attention. Currently, [¹⁸F]FDG-PET/CT radiomics is growingly evaluated in lung cancer to discover if it enhances the diagnostic performance or implication of [¹⁸F]FDG-PET/CT in the management of lung cancer. In this review, we provide a short overview of the technical aspects, as they are discussed in different articles of this special issue. We mainly focus on the diagnostic performance of the [¹⁸F]FDG-PET/CT-based radiomics and the role of artificial intelligence in non-small cell lung cancer, impacting the early detection, staging, prediction of tumor subtypes, biomarkers, and patient's outcomes.

Percutaneous Image-Guided Biopsy for a Comprehensive Hybridization Capture-Based Next-Generation Sequencing in Primary Lung Cancer: Safety, Efficacy, and Predictors of Outcome

Introduction

To evaluate factors associated with successful comprehensive genomic sequencing of image-guided percutaneous needle biopsies in patients with lung cancer using a broad hybrid capture-based next-generation sequencing assay (CHCA).

Methods

We conducted a single-institution retrospective review of image-guided percutaneous transthoracic needle biopsies from January 2018 to December 2019. Samples with confirmed diagnosis of primary lung cancer and for which CHCA had been attempted were identified. Pathologic, clinical data and results of the CHCA were reviewed. Covariates associated with CHCA success were tested for using Fisher's exact test or Wilcoxon ranked sum test. Logistic regression was used to identify factors independently associated with likelihood of CHCA success.

Results

CHCA was requested for 479 samples and was successful for 433 (91%), with a median coverage depth of 659X. Factors independently associated with lower likelihood of CHCA success included small tumor size (OR = 0.26 [95% confidence interval (CI): 0.11-0.62, p = 0.002]), intraoperative inadequacy on cytologic assessment (OR = 0.18 [95% CI: 0.06-0.63, p = 0.005]), small caliber needles (≥20-gauge) (OR = 0.22 [95% CI: 0.10-0.45, p < 0.001]), and presence of lung parenchymal abnormalities (OR = 0.12 [95% CI: 0.05-0.25, p < 0.001]). Pneumothorax requiring chest tube insertion occurred in 6% of the procedures. No grade IV complications or procedure-related deaths were reported.

Conclusions

Percutaneous image-guided transthoracic needle biopsy is safe and has 91% success rate for CHCA in primary lung cancer. Intraoperative inadequacy, small caliber needle, presence of parenchymal abnormalities, and small tumor size (≤1 cm) are independently associated with likelihood of failure.

Predictive model for the probability of malignancy in solitary pulmonary nodules: a meta-analysis

BACKGROUND

To date, multiple predictive models have been developed with the goal of reliably differentiating between solitary pulmonary nodules (SPNs) that are malignant and those that are benign. The present meta-analysis was conducted to assess the diagnostic utility of these predictive models in the context of SPN differential diagnosis.

METHODS

The PubMed, Embase, Cochrane Library, CNKI, Wanfang, and VIP databases were searched for relevant studies published through August 31, 2021. Pooled data analyses were conducted using Stata v12.0.

RESULTS

In total, 20 retrospective studies that included 5171 SPNs (malignant/benign: 3662/1509) were incorporated into this meta-analysis. Respective pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic score values were 88% (95CI%: 0.84-0.91), 78% (95CI%: 0.74-0.80), 3.91 (95CI%: 3.42-4.46), 0.16 (95CI%: 0.12-0.21), and 3.21 (95CI%: 2.87-3.55), with an area under the summary receiver operating characteristic curve value of 86% (95CI%: 0.83-0.89). Significant heterogeneity among studies was detected with respect to sensitivity (I² = 89.07%), NLR (I² = 87.29%), and diagnostic score (I² = 72.28%). In a meta-regression analysis, sensitivity was found to be impacted by the standard reference in a given study (surgery and biopsy vs. surgery only, P = 0.02), while specificity was impacted by whether studies were blinded (yes vs. unclear, P = 0.01). Sensitivity values were higher when surgery and biopsy samples were used as a standard reference, while unclear blinding status was associated with increased specificity. No significant evidence of publication bias was detected for the present meta-analysis (P = 0.539).

CONCLUSIONS

The results of this meta-analysis demonstrate that predictive models can offer significant diagnostic utility when establishing whether SPNs are malignant or benign.

Application Value of PET/CT and MRI in the Diagnosis and Treatment of Patients With Synchronous Multiple Pulmonary Ground-Glass Nodules

Background

Synchronous multiple ground-glass nodules (SMGGNs) in synchronous multiple lung cancers are associated with specific imaging findings. It is difficult to distinguish whether multiple nodules are primary tumors or metastatic lesions in the lungs. The need for PET/CT and contrast-enhanced brain MRI for these patients remains unclear. This study investigated the necessity of these two imaging examinations for SMGGN patients by means of retrospective analysis.

Methods

SMGGN patients who were diagnosed and treated in our hospital from October 2017 to May 2020 and underwent whole-body PET/CT(Cranial excepted) and/or contrast-enhanced brain MRI+DWI were enrolled in this study. We analyzed the imaging and clinical characteristics of these patients to evaluate SMGGN patients’ need to undergo whole-body PET/CT and brain MRI examination.

Results

A total of 87 SMGGN patients were enrolled. 51 patients underwent whole-body PET/CT examinations and did not show signs of primary tumors in other organs, metastatic foci in other organs, or metastasis to surrounding lymph nodes. 87 patients underwent whole-brain MRI, which did not reveal brain metastases but did detect an old cerebral infarction in 23 patients and a new cerebral infarction in one patient. 87 patients underwent surgical treatment in which 219 nodules were removed. All nodules were diagnosed as adenocarcinoma or atypical adenomatous hyperplasia. No lymph node metastasis was noted.

Conclusion

For SMGGN patients, PET/CT and enhanced cranial MRI are unnecessary for SMGGNs patients, but from the perspective of perioperative patient safety, preoperative MRI+DWI examination is recommended for SMGGNs patients.

Pearls and Pitfalls in Lung Cancer Imaging

Imaging plays an essential role in the diagnosis and staging of pulmonary malignancy. Familiarity of less common manifestations of lung cancer including subsolid nodule, consolidation, and cyst associated lung cancer is important to avoid delayed diagnosis and improve patient outcomes. In this article, we review the staging of multifocal lung cancer, PET negative lung cancers (carcinoid and indolent lung adenocarcinoma), and false positive lymph nodes on PET due to infectious and inflammatory etiologies. Knowledge of these potential pitfalls and pearls in lung cancer imaging and correlation with patients' clinical history are essential to prevent misinterpretation.

Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules

Purpose

This work aims to train, validate, and test a dual-stream three-dimensional convolutional neural network (3D-CNN) based on fluorine 18 (¹⁸F)-fluorodeoxyglucose (FDG) PET/CT to distinguish benign lesions and invasive adenocarcinoma (IAC) in ground-glass nodules (GGNs).

Methods

We retrospectively analyzed patients with suspicious GGNs who underwent ¹⁸F-FDG PET/CT in our hospital from November 2011 to November 2020. The patients with benign lesions or IAC were selected for this study. According to the ratio of 7:3, the data were randomly divided into training data and testing data. Partial image feature extraction software was used to segment PET and CT images, and the training data after using the data augmentation were used for the training and validation (fivefold cross-validation) of the three CNNs (PET, CT, and PET/CT networks).

Results

A total of 23 benign nodules and 92 IAC nodules from 106 patients were included in this study. In the training set, the performance of PET network (accuracy, sensitivity, and specificity of 0.92 ± 0.02, 0.97 ± 0.03, and 0.76 ± 0.15) was better than the CT network (accuracy, sensitivity, and specificity of 0.84 ± 0.03, 0.90 ± 0.07, and 0.62 ± 0.16) (especially accuracy was significant, P-value was 0.001); in the testing set, the performance of both networks declined. However, the accuracy and sensitivity of PET network were still higher than that of CT network (0.76 vs. 0.67; 0.85 vs. 0.70). For dual-stream PET/CT network, its performance was almost the same as PET network in the training set (P-value was 0.372–1.000), while in the testing set, although its performance decreased, the accuracy and sensitivity (0.85 and 0.96) were still higher than both CT and PET networks. Moreover, the accuracy of PET/CT network was higher than two nuclear medicine physicians [physician 1 (3-year experience): 0.70 and physician 2 (10-year experience): 0.73].

Conclusion

The 3D-CNN based on ¹⁸F-FDG PET/CT can be used to distinguish benign lesions and IAC in GGNs, and the performance is better when both CT and PET images are used together.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40658-021-00423-1.

Ultrashort echo time MRI of the lung in children and adolescents: comparison with non-enhanced computed tomography and standard post-contrast T1w MRI sequences

Objectives

To compare the diagnostic value of ultrashort echo time (UTE) magnetic resonance imaging (MRI) for the lung versus the gold standard computed tomography (CT) and two T1-weighted MRI sequences in children.

Methods

Twenty-three patients with proven oncologic disease (14 male, 9 female; mean age 9.0 + / − 5.4 years) received 35 low-dose CT and MRI examinations of the lung. The MRI protocol (1.5-T) included the following post-contrast sequences: two-dimensional (2D) incoherent gradient echo (GRE; acquisition with breath-hold), 3D volume interpolated GRE (breath-hold), and 3D high-resolution radial UTE sequences (performed during free-breathing). Images were evaluated by considering image quality as well as distinct diagnosis of pulmonary nodules and parenchymal areal opacities with consideration of sizes and characterisations.

Results

The UTE technique showed significantly higher overall image quality, better sharpness, and fewer artefacts than both other sequences. On CT, 110 pulmonary nodules with a mean diameter of 4.9 + / − 2.9 mm were detected. UTE imaging resulted in a significantly higher detection rate compared to both other sequences (p < 0.01): 76.4% (84 of 110 nodules) for UTE versus 60.9% (67 of 110) for incoherent GRE and 62.7% (69 of 110) for volume interpolated GRE sequences. The detection of parenchymal areal opacities by the UTE technique was also significantly higher with a rate of 93.3% (42 of 45 opacities) versus 77.8% (35 of 45) for 2D GRE and 80.0% (36 of 45) for 3D GRE sequences (p < 0.05).

Conclusion

The UTE technique for lung MRI is favourable in children with generally high diagnostic performance compared to standard T1-weighted sequences as well as CT.

Key Points

• Due to the possible acquisition during free-breathing of the patients, the UTE MRI sequence for the lung is favourable in children.

• The UTE technique reaches higher overall image quality, better sharpness, and lower artefacts, but not higher contrast compared to standard post-contrast T1-weighted sequences.

• In comparison to the gold standard chest CT, the detection rate of small pulmonary nodules small nodules ≤ 4 mm and subtle parenchymal areal opacities is higher with the UTE imaging than standard T1-weighted sequences.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-08236-7.

Diagnosis of Peripheral Pulmonary Lesions with Transbronchial Lung Cryobiopsy by Guide Sheath and Radial Endobronchial Ultrasonography: A Prospective Control Study

Objective

We design a prospective control study on the utilization of transbronchial cryobiopsy guided by EBUS-GS (EBUS-GS-TBCB) to diagnose PPLs.

Methods

PPLs were defined as pulmonary nodules or masses with a diameter from 10 mm to 50 mm. PPLs were randomly divided into group EBUS-GS-TBCB and transbronchial biopsy by forceps guided under EBUS-GS (EBUS-GS-TBB).

Results

28 cases were involved in group EBUS-GS-TBCB and 31 cases were in group EBUS-GS-TBB. The mean sizes of PPLs were 30.23 ± 11.10 mm in group EBUS-GS-TBCB and 28.69 ± 8.62 mm in group EBUS-GS-TBB (t = 0.600, p=0.551). The diagnostic yields of EBUS-GS-TBCB and EBUS-GS-TBB were 75% and 64.52% respectively, and the difference between the two groups was not significant (χ ² value = 0.137, p=0.711). If only the first specimen was taken into account, the diagnostic yields from EBUS-GS-TBCB and EBUS-GS-TBB were 64.29% (18/28 cases) and 35.48% (11/31 cases), respectively. The difference was statistically significant by Fisher's Exact Test (χ ² value = 4.883, p=0.038). The total incidence rates of bleeding were 21.43% and 6.45%, respectively, in groups EBUS-GS-TBCB and EBUS-GS-TBB. The total incidence rates of pneumothorax were 7.14% and 0, respectively, in groups EBUS-GS-TBCB and EBUS-GS-TBB.

Conclusion

The diagnostic yield of EBUS-GS-TBCB was slightly higher than that of EBUS-GS-TBB for the diagnosis of PPLs. EBUS-GS-TBCB might be useful if only the first sample was taken into account.

Clinical-radiological predictive model in differential diagnosis of small (≤ 20 mm) solitary pulmonary nodules

BACKGROUND

There is a lack of clinical-radiological predictive models for the small (≤ 20 mm) solitary pulmonary nodules (SPNs). We aim to establish a clinical-radiological predictive model for differentiating malignant and benign small SPNs.

MATERIALS AND METHODS

Between January 2013 and December 2018, a retrospective cohort of 250 patients with small SPNs was used to construct the predictive model. A second retrospective cohort of 101 patients treated between January 2019 and December 2020 was used to independently test the model. The model was also compared to two other models that had previously been identified.

RESULTS

In the training group, 250 patients with small SPNs including 156 (62.4%) malignant SPNs and 94 (37.6%) benign SPNs patients were included. Multivariate logistic regression analysis indicated that older age, pleural retraction sign, CT bronchus sign, and higher CEA level were the risk factors of malignant small SPNs. The predictive model was established as: X = - 10.111 + [0.129 × age (y)] + [1.214 × pleural retraction sign (present = 1; no present = 0)] + [0.985 × CT bronchus sign (present = 1; no present = 0)] + [0.21 × CEA level (ug/L)]. Our model had a significantly higher region under the receiver operating characteristic (ROC) curve (0.870; 50% CI: 0.828-0.913) than the other two models.

CONCLUSIONS

We established and validated a predictive model for estimating the pre-test probability of malignant small SPNs, that can help physicians to choose and interpret the outcomes of subsequent diagnostic tests.

Prediction of malignancy for solitary pulmonary nodules based on imaging, clinical characteristics and tumor marker levels

OBJECTIVE

To establish a prediction model of malignancy for solitary pulmonary nodules (SPNs) on the basis of imaging, clinical characteristics and tumor marker levels.

METHODS

Totally, 341 cases of SPNs were enrolled in this retrospective study, in which 70% were selected as the training group (n = 238) and the rest 30% as the verification group (n = 103). The imaging, clinical characteristics and tumor marker levels of patients with benign and malignant SPNs were compared. Influencing factors were identified using multivariate logistic regression analysis. The model was assessed by the area under the curve (AUC) of the receiver operating characteristic curve.

RESULTS

Differences were evident between patients with benign and malignant SPNs in age, gender, smoking history, carcinoembryonic antigen (CEA), neuron-specific enolase, nodule location, edge smoothing, spiculation, lobulation, vascular convergence sign, air bronchogram, ground-glass opacity, vacuole sign and calcification (all P < 0.05). Influencing factors for malignancy included age, gender, nodule location, spiculation, vacuole sign and CEA (all P < 0.05). The established model was as follows: Y = -5.368 + 0.055 × age + 1.012 × gender (female = 1, male = 0) + 1.302 × nodule location (right upper lobe = 1, others = 0) + 1.208 × spiculation (yes = 1, no = 0) + 2.164 × vacuole sign (yes = 1, no = 0) -0.054 × CEA. The AUC of the model with CEA was 0.818 (95% confidence interval, 0.763-0.865), with a sensitivity of 64.80% and a specificity of 84.96%, and the stability was better through internal verification.

CONCLUSIONS

The prediction model established in our study exhibits better accuracy and internal stability in predicting the probability of malignancy for SPNs.

Lung nodule malignancy classification with weakly supervised explanation generation

Purpose: Explainable AI aims to build systems that not only give high performance but also are able to provide insights that drive the decision making. However, deriving this explanation is often dependent on fully annotated (class label and local annotation) data, which are not readily available in the medical domain. Approach: This paper addresses the above-mentioned aspects and presents an innovative approach to classifying a lung nodule in a CT volume as malignant or benign, and generating a morphologically meaningful explanation for the decision in the form of attributes such as nodule margin, sphericity, and spiculation. A deep learning architecture that is trained using a multi-phase training regime is proposed. The nodule class label (benign/malignant) is learned with full supervision and is guided by semantic attributes that are learned in a weakly supervised manner. Results: Results of an extensive evaluation of the proposed system on the LIDC-IDRI dataset show good performance compared with state-of-the-art, fully supervised methods. The proposed model is able to label nodules (after full supervision) with an accuracy of 89.1% and an area under curve of 0.91 and to provide eight attributes scores as an explanation, which is learned from a much smaller training set. The proposed system's potential to be integrated with a sub-optimal nodule detection system was also tested, and our system handled 95% of false positive or random regions in the input well by labeling them as benign, which underscores its robustness. Conclusions: The proposed approach offers a way to address computer-aided diagnosis system design under the constraint of sparse availability of fully annotated images.

Performance Evaluation of a Deep Learning System for Differential Diagnosis of Lung Cancer With Conventional CT and FDG PET/CT Using Transfer Learning and Metadata

PURPOSE

We aimed to evaluate the performance of a deep learning system for differential diagnosis of lung cancer with conventional CT and FDG PET/CT using transfer learning (TL) and metadata.

METHODS

A total of 359 patients with a lung mass or nodule who underwent noncontrast chest CT and FDG PET/CT prior to treatment were enrolled retrospectively. All pulmonary lesions were classified by pathology (257 malignant, 102 benign). Deep learning classification models based on ResNet-18 were developed using the pretrained weights obtained from ImageNet data set. We propose a deep TL model for differential diagnosis of lung cancer using CT imaging data and metadata with SUVmax and lesion size derived from PET/CT. The area under the receiver operating characteristic curve (AUC) of the deep learning model was measured as a performance metric and verified by 5-fold cross-validation.

RESULTS

The performance metrics of the conventional CT model were generally better than those of the CT of PET/CT model. Introducing metadata with SUVmax and lesion size derived from PET/CT into baseline CT models improved the diagnostic performance of the CT of PET/CT model (AUC = 0.837 vs 0.762) and the conventional CT model (AUC = 0.877 vs 0.817).

CONCLUSIONS

Deep TL models with CT imaging data provide good diagnostic performance for lung cancer, and the conventional CT model showed overall better performance than the CT of PET/CT model. Metadata information derived from PET/CT can improve the performance of deep learning systems.

CT-Guided Transthoracic Biopsy of Lung Lesions Using a Non-Coaxial Biopsy Needle Technique: CT Characteristics Predictive for Diagnostic Accuracy and Pneumothorax

Objectives

To analyze computed tomography (CT) characteristics predictive for diagnostic accuracy and pneumothorax in CT fluoroscopy-guided transthoracic biopsy (CTF-TTB) of lung lesions using non-coaxial biopsy needle technique.

Methods

Retrospectively 274 lung lesion biopsies with confirmed histology were included in our study. CTF-TTB was done using an 18-gauge non-coaxial cutting needle. Diagnostic accuracy rates were calculated per lesion size and CT and procedural characteristics were evaluated for their predictive value regarding diagnostic accuracy and development of pneumothorax (maximal nodule diameter, distance to pleura, location per lung segment, nodule composition, benign versus malignant histology, and number of specimens).

Results

Overall diagnostic accuracy of CTF-TTB was high (93%). Diagnostic accuracy for lesions ≤10 mm was 81%. Maximal nodule diameter was the only predictive CT characteristic for diagnostic success (p = 0.03). Pneumothorax occurred in 27%. Distance of lesion to pleura was the only risk factor for pneumothorax (p < 0.00001). Pneumothorax rates were significantly lower in subpleural lesions (14%) compared to those located 1-10 mm (47%), 10-20 mm (33%), and >20 mm from pleura (29%).

Conclusions

High diagnostic accuracy rates were achieved with CTF-TTB using non-coaxial biopsy technique, even for lesions ≤10 mm. Pneumothorax rates were comparable with other studies. Lesion size was the only predictive CT characteristic for diagnostic accuracy. Distance to pleura was the only risk factor for pneumothorax.

CT-guided biopsy of lung nodules with pleural contact: Comparison of two puncture routes

PURPOSE

The purpose of this study was to retrospectively compare two puncture routes (transpleural vs. transpulmonary) for computed tomography (CT) fluoroscopy-guided cutting needle biopsy of lung nodules with pleural contact.

PATIENTS AND METHODS

A total of 102 patients (72 men; mean age, 71.1±9.5 [SD] years) were included and 102 biopsies of 102 lung nodules (mean size, 16.7±5.9 [SD] mm; range, 6.0-29.4mm; mean length of pleural contact, 10.1±4.2 [SD] mm; range, 2.8-19.6mm) were analyzed. All procedures were classified as biopsies via the direct transpleural route or the transpulmonary route. The patient-, lesion-, and biopsy-related variables, diagnostic yields, and incidence of complications were compared between the two routes.

RESULTS

Biopsy was performed via the direct transpleural route (n=59; 57.8%) and transpulmonary route (n=43; 42.2%). In the transpulmonary route group, the mean distance of the intrapulmonary pathway was 17.7±9.4 [SD] mm (range: 4.1-47.6mm; P<0.001) and the introducer needle trajectory angle of<45° was significantly observed (8.5% [5/59] vs. 60.5% [26/43]; P<0.001). There was no significant difference in diagnostic accuracy between the direct transpleural and transpulmonary routes (93.2% [55/59] vs. 90.7% [39/43]; P=0.718). The frequencies of all complications (64.4% [38/59] vs. 97.7% [42/43]; P<0.001), pneumothorax (33.9% [20/59] vs. 65.1% [28/43]; P=0.003), pneumothorax with chest tube placement (3.4% [2/59] vs. 18.6% [8/43]; P=0.016), and pulmonary hemorrhage (47.5% [28/59] vs. 76.7% [33/43]; P=0.004) were significantly lower in the direct transpleural group.

CONCLUSION

Direct transpleural route is recommended for CT fluoroscopy-guided biopsy of lung nodules with pleural contact because it is safer and yields similar diagnostic accuracy than transpulmonary route.

Using a risk model for probability of cancer in pulmonary nodules

Background

Considering the high morbidity and mortality of lung cancer and the high incidence of pulmonary nodules, clearly distinguishing benign from malignant lung nodules at an early stage is of great significance. However, determining the kind of lung nodule which is more prone to lung cancer remains a problem worldwide.

Methods

A total of 480 patients with pulmonary nodule data were collected from Shandong, China. We assessed the clinical characteristics and computed tomography (CT) imaging features among pulmonary nodules in patients who had undergone video‐assisted thoracoscopic surgery (VATS) lobectomy from 2013 to 2018. Preliminary selection of features was based on a statistical analysis using SPSS. We used WEKA to assess the machine learning models using its multiple algorithms and selected the best decision tree model using its optimization algorithm.

Results

The combination of decision tree and logistics regression optimized the decision tree without affecting its AUC. The decision tree structure showed that lobulation was the most important feature, followed by spiculation, vessel convergence sign, nodule type, satellite nodule, nodule size and age of patient.

Conclusions

Our study shows that decision tree analyses can be applied to screen individuals for early lung cancer with CT. Our decision tree provides a new way to help clinicians establish a logical diagnosis by a stepwise progression method, but still needs to be validated for prospective trials in a larger patient population.

Outcome of PET-Negative Solid Pulmonary Nodules: A Retrospective Study

OBJECTIVE

It was observed that malignancy had been found on follow-up in patients with PET-negative solid solitary pulmonary nodules (SPN). A retrospective analysis was performed to observe the natural history and malignant potential of these lesions, which, in routine practice, are presumed to be inactive.

MATERIALS AND METHODS

Patients with an incidentally-discovered solid solitary pulmonary nodule who then had a negative follow-up PET/CT from 2005 to 2015 were identified using a text-based search methodology. These patients' charts were mined to determine the rate of development of subsequent malignancy from these index nodules.

RESULTS

Of the patients with initially PET-negative solitary pulmonary nodule (n = 62, 43.5% women, mean age 65), 44 had clinical follow-up of the index lesion. 8 (7 pathology-proven) subsequent malignancies were identified with a mean time to diagnosis of 37.6 (±31.3) months. There were no statistically significant predictors of subsequent development of cancer (including age, gender, and smoking status).

CONCLUSION

Upon follow up, 18.2% of the initially queried solid PET-negative nodules developed subsequent malignancy at an average time of 37.6 months, suggesting the continued need for follow-up of these initially PET-negative nodules beyond the 2 years currently suggested in popular guidelines. Importantly, these findings also remind radiologists that a negative PET/CT is not a surrogate for tissue diagnosis in the case of non-FDG avid SPN.

Safety and Diagnostic Yield of Transthoracic Needle Aspiration of the Lung in Elderly Patients

BACKGROUND

Pulmonary nodules in elderly patients are commonly encountered in clinical practice. Tissue sampling with image guided transthoracic needle aspiration is often performed but may be complicated by pneumothorax or bleeding. To understand the outcomes of transthoracic needle aspiration in the elderly, we retrospectively reviewed outcomes of patients age 75 or greater in a single tertiary center.

METHODS

Four-hundred eleven patients age 75 or greater with a pulmonary nodule identified on computed tomography who underwent needle aspiration of the lung were studied. Diagnostic yield and procedural complications were assessed for each patient and subgroups analysis of those age 85 or greater was performed.

RESULTS

Malignancy was confirmed in 70% of subjects and a benign diagnosis identified in 9%. Of the 411 patents, 203 (49.4%) experienced a complication; 150 patients (36.5%) developed a pneumothorax and 79 (19.2%) had bleeding. No patient required transfusion, experienced persistent air leak or massive hemoptysis, air embolism or death. Post procedural hospitalization was required in 36 patients (8.8%) with a median hospital stay of 2 days. No factors were identified to be associated with occurrence of a complication (all p ≥ 0.16) and complications were not increased in those 85 or greater.

CONCLUSION

Our results suggest that in an elderly population, image guided needle aspiration of a pulmonary nodule provides diagnostic findings in most patients. Procedural complications following are not uncommon but the severity and long-term impact are limited. The occurrence of complications is similar in those age 75-84 and age 85 and older.

Development and validation of a clinically applicable deep learning strategy (HONORS) for pulmonary nodule classification at CT: A retrospective multicentre study

PURPOSE

To propose a practical strategy for the clinical application of deep learning algorithm, i.e., Hierarchical-Ordered Network-ORiented Strategy (HONORS), and a new approach to pulmonary nodule classification in various clinical scenarios, i.e., Filter-Guided Pyramid NETwork (FGP-NET).

MATERIALS AND METHODS

We developed and validated FGP-NET on a collection of 2106 pulmonary nodules on computed tomography images which combined screened and clinically detected nodules, and performed external test (n = 341). The area under the curves (AUCs) of FGP-NET were assessed. A comparison study with a group of 126 skilled radiologists was conducted. On top of FGP-NET, we built up our HONORS which was composed of two solutions. In the Human Free Solution, we used the high sensitivity operating point for screened nodules, but the high specificity operating point for clinically detected nodules. In the Human-Machine Coupling Solution, we used the Youden point.

RESULTS

FGP-NET achieved AUCs of 0.969 and 0.847 for internal and external test. The AUCs of the subsets of the external test set ranged from 0.890 to 0.942. The average sensitivity and specificity of the 126 radiologists were 72.2 ± 15.1 % and 71.7 ± 15.5 %, respectively, while a higher sensitivity (93.3 %) but a relatively inferior specificity (64.0 %) were achieved by FGP-NET. HONORS-guided FGP-NET identified benign nodules with high sensitivity (sensitivity,95.5 %; specificity, 72.5 %) in the screened nodules, and identified malignant nodules with high specificity (sensitivity, 31.0 %; specificity, 97.5 %) in the clinically detected nodules. These nodules could be reliably diagnosed without any intervention from radiologists, via the Human Free Solution. The remaining ambiguous nodules were diagnosed with high performance, which however required manual confirmation by radiologists, via the Human-Machine Coupling Solution.

CONCLUSIONS

FGP-NET performed comparably to skilled radiologists in terms of diagnosing pulmonary nodules. HONORS, due to its high performance, might reliably contribute a second opinion, aiding in optimizing the clinical workflow.

The Value of a Seven-Autoantibody Panel Combined with the Mayo Model in the Differential Diagnosis of Pulmonary Nodules

Background

Identifying malignant pulmonary nodules and detecting early-stage lung cancer (LC) could reduce mortality. This study investigated the clinical value of a seven-autoantibody (7-AAB) panel in combination with the Mayo model for the early detection of LC and distinguishing benign from malignant pulmonary nodules (MPNs).

Methods

The concentrations of the elements of a 7-AAB panel were quantitated by enzyme-linked immunosorbent assay (ELISA) in 806 participants. The probability of MPNs was calculated using the Mayo predictive model. The performances of the 7-AAB panel and the Mayo model were analyzed by receiver operating characteristic (ROC) analyses, and the difference between groups was evaluated by chi-square tests (χ²).

Results

The combined area under the ROC curve (AUC) for all 7 AABs was higher than that of a single one. The sensitivities of the 7-AAB panel were 67.5% in the stage I-II LC patients and 60.3% in the stage III-IV patients, with a specificity of 89.6% for the healthy controls and 83.1% for benign lung disease patients. The detection rate of the 7-AAB panel in the early-stage LC patients was higher than that of traditional tumor markers. The AUC of the 7-AAB panel in combination with the Mayo model was higher than that of the 7-AAB panel alone or the Mayo model alone in distinguishing MPN from benign nodules. For early-stage MPN, the sensitivity and specificity of the combination were 93.5% and 58.0%, respectively. For advanced-stage MPN, the sensitivity and specificity of the combination were 91.4% and 72.8%, respectively. The combination of the 7-AAB panel with the Mayo model significantly improved the detection rate of MPN, but the positive predictive value (PPV) and the specificity were not improved when compared with either the 7-AAB panel alone or the Mayo model alone.

Conclusion

Our study confirmed the clinical value of the 7-AAB panel for the early detection of lung cancer and in combination with the Mayo model could be used to distinguish benign from malignant pulmonary nodules.

The application of dual-layer spectral detector computed tomography in solitary pulmonary nodule identification

Background

Differentiating between malignant solitary pulmonary nodules (SPNs) and other lung diseases remains a substantial challenge. The latest generation of dual-energy computed tomography (CT), which realizes dual-energy technology at the detector level, has clinical potential for distinguishing lung cancer from other benign SPNs. This study aimed to evaluate the performance of dual-layer spectral detector CT (SDCT) for the differentiation of SPNs.

Methods

Spectral images of 135 SPNs confirmed by pathology were retrospectively analyzed in both the arterial phase (AP) and the venous phase (VP). Patients were classified into two groups [the malignant group (n=93) and the benign group (n=42)], with the malignant group further divided into small cell lung cancer (SCLC, n=30) and non-small cell lung cancer (NSCLC, n=63) subtypes. The slope of the spectral Hounsfield Unit (HU) curve (λ_HU), normalized iodine concentration (NIC), CT values of 40 keV monochromatic images (CT_40keV), and normalized arterial enhancement fraction (NAEF) in contrast-enhanced images were calculated and compared between the benign and malignant groups, as well as between the SCLC and NSCLC subgroups. ROC curve analysis was performed to assess the diagnostic performance of the above parameters. Seventy cases were randomly selected and independently measured by two radiologists, and intraclass correlation coefficient (ICC) and Bland-Altman analyses were performed to calculate the reliability of the measurements.

Results

Except for NAEF (P=0.23), the values of the parameters were higher in the malignant group than in the benign group (all P<0.05). NIC, λ_HU, and CT_40keV performed better in the VP (NIC_VP, λ_VPHU, and CT_VP40keV) (P<0.001), with an area under the ROC curve (AUC) of 0.93, 0.89, and 0.89 respectively. With respective cutoffs of 0.31, 1.83, and 141.00 HU, the accuracy of NIC_VP, λ_VPHU, and CT_VP40keV was 91.11%, 85.19%, and 88.15%, respectively. In the subgroup differentiating NSCLC and SCLC, the diagnostic performances of NIC_AP (AUC =0.89) were greater than other parameters. NIC_AP had an accuracy of 86.02% when the cutoff was 0.14. ICC and Bland-Altman analyses indicated that the measurement of SDCT has great reproducibility.

Conclusions

Quantitative measures from SDCT can help to differentiate benign from malignant SPNs and may help with the further subclassification of malignant cancer into SCLC and NSCLC.

Digital tomosynthesis and ground glass nodules: Optimization of acquisition protocol. A phantom study

INTRODUCTION

Ground-glass nodules may be the expression of benign conditions, pre-invasive lesions or malignancies. The aim of our study was to evaluate the capability of chest digital tomosynthesis (DTS) in detecting pulmonary ground-glass opacities (GGOs).

METHODS

An anthropomorphic chest phantom and synthetic nodules were used to simulate pulmonary ground-glass nodules. The nodules were positioned in 3 different regions (apex, hilum and basal); then the phantom was scanned by multi-detector CT (MDCT) and DTS. For each set (nodule-free phantom, nodule in apical zone, nodule in hilar zone, nodule in basal zone) seven different scans (n = 28) were performed varying the following technical parameters: Cu-filter (0.1-0.3 mm), dose rateo (10-25) and X-ray tube voltage (105-125 kVp). Two radiologists in consensus evaluated the DTS images and provided in agreement a visual score: 1 for unidentifiable nodules, 2 for poorly identifiable nodules, 3 for nodules identifiable with fair certainty, 4 for nodules identifiable with absolute certainty.

RESULTS

Increasing the dose rateo from 10 to 15, GGOs located in the apex and in the basal zone were better identified (from a score = 2 to a score = 3). GGOs located in the hilar zone were not visible even with a higher dose rate. Intermediate density GGOs had a good visibility score (score = 3) and it did not improve by varying technical parameters. A progressive increase of voltage (from 105 kVp to 125 kVp) did not provide a better nodule visibility.

CONCLUSION

DTS with optimized technical parameters can identify GGOs, in particular those with a diameter greater than 10 mm.

IMPLICATIONS FOR PRACTICE

DTS could have a role in the follow-up of patients with known GGOs identified in lung apex or base region.

Status of pleural lavage cytology in cases of pulmonary metastasis: a single-institution retrospective study

OBJECTIVE

In primary lung cancer surgery, intraoperative pleural lavage cytology (PLC) has been identified as a prognostic marker. However, there have been no reports on PLC for pulmonary metastasectomies. Therefore, the impact of PLC status for patients undergoing pulmonary metastasectomy remains unknown. We aimed to know the incidence and prognostic impact of positive PLC findings in pulmonary metastasectomies.

METHODS

We retrospectively reviewed patients undergoing pulmonary metastasectomies between January 2013 and December 2018. One hundred and eighty-nine PLC procedures in 159 patients undergoing pulmonary metastasectomy were analyzed. Follow-up information was available in 107 patients who underwent 132 procedure, and they were investigated for pleural recurrence-free probability (PRFP) and overall survival (OS) after pulmonary metastasectomy.

RESULTS

The type of primary lesion was colorectal cancer (101/189, 53%), urothelial/kidney cancer (17/189, 9%), skeletal/soft tissue tumor (18/189, 10%), hepatobiliary/pancreatic cancer (19/189, 10%), uterine/ovarian cancer (14/189, 7%), otorhinolaryngological cancer (11/189, 6%), and other minor lesions (9/189, 5%). Nine PLC-positive metastasectomies were revealed (9/189, 4.8%). They consisted of six metastasectomies from pancreatic cancer, two from osteosarcoma, and one from tongue cancer. Significant predictors for PLC status was type of primary tumor (P < 0.001). PRFP and OS rate of PLC-positive group were significantly lower than PLC-negative (P < 0.001, respectively).

CONCLUSION

PLC-positive results were rarely seen in pulmonary metastasectomies. PLC status was associated with the incidence of ipsilateral pleural recurrence and survival after metastasectomy. Cytologic examination of PLC should be considered in patients undergoing pulmonary metastasectomy.

68Ga-FAPI PET/CT Improves Therapeutic Strategy by Detecting a Second Primary Malignancy in a Patient With Rectal Cancer

A 71-year-old man with pathologically confirmed rectal cancer underwent F-FDG PET/CT before radical operation, which showed multiple nodules with low uptake in bilateral pleura and 1 solitary pulmonary nodule with slight uptake in left lung. The subpleural nodule was diagnosed as benign lesion through the biopsy. Ga-labeled fibroblast-activation-protein inhibitor PET/CT was performed for further evaluation, which showed low uptake in bilateral subpleural nodules but focally increased uptake in the nodule of left lung. This nodule was found to be a primary lung adenocarcinoma by the CT-guided biopsy. A diagnosis of rectum and lung double primary malignancies was finally made.

Value of Shape and Texture Features from 18F-FDG PET/CT to Discriminate between Benign and Malignant Solitary Pulmonary Nodules: An Experimental Evaluation

In this paper, we investigate the role of shape and texture features from 18F-FDG PET/CT to discriminate between benign and malignant solitary pulmonary nodules. To this end, we retrospectively evaluated cross-sectional data from 111 patients (64 males, 47 females, age = 67.5 ± 11.0) all with histologically confirmed benign (n=39) or malignant (n=72) solitary pulmonary nodules. Eighteen three-dimensional imaging features, including conventional, texture, and shape features from PET and CT were tested for significant differences (Wilcoxon-Mann-Withney) between the benign and malignant groups. Prediction models based on different feature sets and three classification strategies (Classification Tree, k-Nearest Neighbours, and Naïve Bayes) were also evaluated to assess the potential benefit of shape and texture features compared with conventional imaging features alone. Eight features from CT and 15 from PET were significantly different between the benign and malignant groups. Adding shape and texture features increased the performance of both the CT-based and PET-based prediction models with overall accuracy gain being 3.4-11.2 pp and 2.2-10.2 pp, respectively. In conclusion, we found that shape and texture features from 18F-FDG PET/CT can lead to a better discrimination between benign and malignant lung nodules by increasing the accuracy of the prediction models by an appreciable margin.

Assessment of Solitary Pulmonary Nodules Based on Virtual Monochrome Images and Iodine-Dependent Images Using a Single-Source Dual-Energy CT with Fast kVp Switching

With lung cancer being the most common malignancy diagnosed worldwide, lung nodule assessment has proved to be one of big challenges of modern medicine. The aim of this study was to examine the usefulness of Dual Energy Computed Tomography (DECT) in solitary pulmonary nodule (SPN) assessment. Between January 2017 and June 2018; 65 patients (42 males and 23 females) underwent DECT scans in the late arterial phase (AP) and venous phase (VP). We concluded that imaging at an energy level of 65 keV was the most accurate in detecting malignancy in solitary pulmonary nodules (SPNs) measuring ≤30 mm in diameter on virtual monochromatic maps. Both virtual monochromatic images and iodine concentration maps prove to be highly useful in differentiating benign and malignant pulmonary nodules. As for iodine concentration maps, the analysis of venous phase images resulted in the highest clinical usefulness. To summarize, DECT may be a useful tool in the differentiation of benign and malignant SPNs. A single-phase DECT examination with scans acquired 90 s after contrast media injection is recommended.

Challenges and countermeasures of thoracic oncology in the epidemic of COVID-19

Since December, 2019, a 2019 novel coronavirus disease (COVID-19) infected by the severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) emerged in Wuhan, Hubei province, and the epidemic situation has continued to spread globally. The epidemic spread of COVID-19 has brought great challenges to the clinical practice of thoracic oncology. Outpatient clinics need to strengthen the differential diagnosis of initial symptoms, pulmonary ground-glass opacity (GGO), consolidation, interstitial and/or interlobular septal thickening, and crazy paving appearance. In the routine of oncology, the differential diagnosis of adverse events from COVID-19 is also significant, including radiation pneumonitis, checkpoint inhibitor pneumonitis (CIP), neutropenic fever, and so on. During the epidemic, indications of transbronchial biopsy (TBB) and CT-guided percutaneous thoracic biopsy are strictly controlled. For patients who are planning to undergo biopsy operation, screening to exclude the possibility of COVID-19 should be carried out. For confirmed or suspected patients, three-level protection should be performed during the operation. Disinfection and isolation measures should be strictly carried out during the operation. At last, more attention to the protection of cancer patients and give priority to the treatment of infected cancer patients.

Predictive model for the diagnosis of benign/malignant small pulmonary nodules

There is some doubt that all nodules <8 mm are really mainly benign and that simple follow-up is adequate in all cases. The purpose of this study is to create a predictive model for the diagnosis of benign and malignant small pulmonary nodules.This was a retrospective case-control study of patients who had undergone pulmonary nodule resection at the Zhejiang University Jinhua Hospital. Patients with pulmonary nodules of ≤10 mm in size on chest high-resolution computed tomography were included. Patients' demographic characteristics, clinical features, and high-resolution computed tomography findings were collected. Logistic regression and receiver-operating characteristic analysis were used to create a predictive model for malignancy.A total of 216 patients were included: 160 with malignant and 56 with benign nodules. Nodule density (odds ratio [OR] = 0.996, 95% confidence interval [CI]: 0.993-0.998, P = .001), vascular penetration sign (OR = 3.49, 95% CI: 1.39-8.76, P = .008), nodule type (OR = 4.27, 95% CI: 1.48-12.29, P = .007), and incisure surrounding nodules (OR = 0.18, 95% CI: 0.04-0.84, P = .03) were independently associated with malignant nodules. These factors were used to create a mathematical model that had an area under the receiver-operating characteristic curve of 0.744. Using a cut-off of 0.762 resulted in 63.1% sensitivity and 75.0% specificity.This study proposes a pulmonary nodule prediction model that can estimate benign/malignant lung nodules with good sensitivity and specificity. Mixed ground-glass nodules, vascular penetration sign, density of lung nodules, and the absence of incisure signs are independently associated with malignant lung nodules.