Assessment Of Pulmonary Nodules Research Articles

Assessment of pulmonary nodules using [18F]-FDG PET/CT in deep inspiration breath-hold.

Our objective was to compare the diagnostic efficacy of [18F]-FDG PET/CT in deep inspiration breath-hold (DIBH) versus free-breathing corrected by software, in the evaluation of PN. We prospectively analyzed 51 patients to assess PN using [18F]-FDG PET/CT in DIBH and free-breathing corrected by software. A total of 84 nodules with an average size of 10 mm were analyzed, with pathological anatomy or medical treatment decisions by a multidisciplinary tumor board used as reference. A total of 84 PN were evaluated, comparing those in DIBH versus free-breathing, finding statistically significant differences in SUVmax values (p < 0.05) (mean SUVmax 3.7 in free-breathing vs. 5.33 in DIBH). When analyzed by location in lobes, we did not find statistically significant differences, though there was a trend towards higher SUVmax values in the lower lobes. [18F]-FDG PET/CT in DIBH showed high sensitivity (95%) and negative predictive value (NPV) (92%), indicating it may be a promising tool for PN characterization. The acquisition of [18F]-FDG PET/CT in DIBH significantly improves the sensitivity and diagnostic efficacy in the assessment of PN. Although no statistically significant differences were found based on location, there is a potential benefit for the lower lobes. These findings could support its use in clinical practice.

Fully automated classification of pulmonary nodules in positron emission tomography-computed tomography imaging using a two-stage multimodal learning approach.

Lung cancer is a malignant tumor, for which pulmonary nodules are considered to be significant indicators. Early recognition and timely treatment of pulmonary nodules can contribute to improving the survival rate of patients with cancer. Positron emission tomography-computed tomography (PET/CT) is a noninvasive, fusion imaging technique that can obtain both functional and structural information of lung regions. However, studies of pulmonary nodules based on computer-aided diagnosis have primarily focused on the nodule level due to a reliance on the annotation of nodules, which is superficial and unable to contribute to the actual clinical diagnosis. The aim of this study was thus to develop a fully automated classification framework for a more comprehensive assessment of pulmonary nodules in PET/CT imaging data. We developed a two-stage multimodal learning framework for the diagnosis of pulmonary nodules in PET/CT imaging. In this framework, Stage I focuses on pulmonary parenchyma segmentation using a pretrained U-Net and PET/CT registration. Stage II aims to extract, integrate, and recognize image-level and feature-level features by employing the three-dimensional (3D) Inception-residual net (ResNet) convolutional block attention module architecture and a dense-voting fusion mechanism. In the experiments, the proposed model's performance was comprehensively validated using a set of real clinical data, achieving mean scores of 89.98%, 89.21%, 84.75%, 93.38%, 86.83%, and 0.9227 for accuracy, precision, recall, specificity, F1 score, and area under curve values, respectively. This paper presents a two-stage multimodal learning approach for the automatic diagnosis of pulmonary nodules. The findings reveal that the main reason for limiting model performance is the nonsolitary property of nodules in pulmonary nodule diagnosis, providing direction for future research.

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.

Evaluation of Pulmonary Nodules by Radiologists vs. Radiomics in Stand-Alone and Complementary CT and MRI.

Increased attention has been given to MRI in radiation-free screening for malignant nodules in recent years. Our objective was to compare the performance of human readers and radiomic feature analysis based on stand-alone and complementary CT and MRI imaging in classifying pulmonary nodules. This single-center study comprises patients with CT findings of pulmonary nodules who underwent additional lung MRI and whose nodules were classified as benign/malignant by resection. For radiomic features analysis, 2D segmentation was performed for each lung nodule on axial CT, T2-weighted (T2w), and diffusion (DWI) images. The 105 extracted features were reduced by iterative backward selection. The performance of radiomics and human readers was compared by calculating accuracy with Clopper-Pearson confidence intervals. Fifty patients (mean age 63 +/- 10 years) with 66 pulmonary nodules (40 malignant) were evaluated. ACC values for radiomic features analysis vs. radiologists based on CT alone (0.68; 95%CI: 0.56, 0.79 vs. 0.59; 95%CI: 0.46, 0.71), T2w alone (0.65; 95%CI: 0.52, 0.77 vs. 0.68; 95%CI: 0.54, 0.78), DWI alone (0.61; 95%CI:0.48, 0.72 vs. 0.73; 95%CI: 0.60, 0.83), combined T2w/DWI (0.73; 95%CI: 0.60, 0.83 vs. 0.70; 95%CI: 0.57, 0.80), and combined CT/T2w/DWI (0.83; 95%CI: 0.72, 0.91 vs. 0.64; 95%CI: 0.51, 0.75) were calculated. This study is the first to show that by combining quantitative image information from CT, T2w, and DWI datasets, pulmonary nodule assessment through radiomics analysis is superior to using one modality alone, even exceeding human readers' performance.

Reduction in Radiologist Interpretation Time of Serial CT and MR Imaging Findings with Deep Learning Identification of Relevant Priors, Series and Finding Locations

Finding comparison to relevant prior studies is a requisite component of the radiology workflow. The purpose of this study was to evaluate the impact of a deep learning tool simplifying this time-consuming task by automatically identifying and displaying the finding in relevant prior studies. The algorithm pipeline used in this retrospective study, TimeLens (TL), is based on natural language processingand descriptor-based image-matching algorithms. The dataset used for testing comprised 3872 series of 246 radiology examinations from 75 patients (189 CTs, 95 MRIs). To ensure a comprehensive testing, five finding types frequently encountered in radiology practice were included: aortic aneurysm, intracranial aneurysm, kidney lesion, meningioma, and pulmonary nodule. After a standardized training session, nine radiologists from three university hospitals performed two reading sessions on a cloud-based evaluation platform resembling a standard RIS/PACS. The task was to measure the diameter of the finding-of-interest on two or more exams (a most recent and at least one prior exam): first without use of TL, and a second session at an interval of at least 21 days with the use of TL. All user actions were logged for each round, including time needed to measure the finding at all timepoints, number of mouse clicks, and mouse distance traveled. The effect of TL was evaluated in total, per finding type, per reader, per experience (resident vs. board-certified radiologist), and per modality. Mouse movement patterns were analyzed with heatmaps. To assess the effect of habituation to the cases, a third round of readings was performed without TL. Across scenarios, TL reduced the average time needed to assess a finding at all timepoints by 40.1% (107 vs. 65seconds; p<0.001). Largest accelerations were demonstrated for assessment of pulmonary nodules (-47.0%; p<0.001). Less mouse clicks (-17.2%) were needed for finding evaluation with TL, and mouse distance traveled was reduced by 38.0%. Time needed to assess the findings increased from round 2 to round 3 (+27.6%; p<0.001). Readers were able to measure a given finding in 94.4% of cases on the series initially proposed by TL as most relevant series for comparison. The heatmaps showed consistently simplified mouse movement patterns with TL. A deep learning tool significantly reduced both the amount of user interactions with the radiology image viewer and the time needed to assess findings of interest on cross-sectional imaging with relevant prior exams.

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.

Contextualizing the Role of Volumetric Analysis in Pulmonary Nodule Assessment: AJR Expert Panel Narrative Review.

Pulmonary nodules are managed based on their size and morphologic characteristics. Radiologists are familiar with assessing nodule size by diameter measured using manually deployed electronic calipers. Size may also be assessed by 3D volumetric measurements (referred to as volumetry) using software. Nodule size and growth are more accurately assessed by volumetry than by diameter, and the evidence supporting clinical use of volumetry has expanded, driven by its use in lung cancer screening (LCS) nodule management algorithms in Europe. The application of volumetry has the potential to reduce recommendations for imaging follow-up of indeterminate solid nodules without impacting cancer detection. Although changes in scanning conditions or volumetry software package may lead to variation in volumetry results, ongoing technical advances have improved the reliability of calculated volumes. Volumetry is now the primary method for determining size of solid nodules in the European LCS position statement and British Thoracic Society recommendations. In this article, we review technical aspects, advantages, and limitations of volumetry, and contextualize its use with respect to its importance relative to morphologic evaluation, role for predicting malignancy in risk models, and practical impact on nodule management, by considering specific scenarios. Implementation challenges and areas requiring further evidence are also highlighted.

Computed tomographic features of pulmonary pure ground-glass nodule: a comparison between neoplastic and non-neoplastic nodules

BackgroundGround-glass nodules (GGNs) are detected more frequently nowadays with the increase in MDCT resolution and applications. On CT GGN is a well circumscribed nodule with mild increase attenuation and not obscuring bronchial and vascular markings. This study aimed to discriminate neoplastic from non-neoplastic pure ground-glass nodules (pGGNs) by performing comparative quantitative and qualitative assessment of main features in computed tomography (CT) chest imaging. This prospective study involved 72 patients, who were referred to perform multidetector computed tomography of the chest in the radiology department. Cases with ground-glass pulmonary nodules were included in the study, and each nodule was assessed and followed for 2 years by a radiomics software for density and histogram analysis and then classified to neoplastic and non-neoplastic nodules. Neoplastic and non-neoplastic nodules morphology and radiomics were compared, and statistical analysis was done.ResultsAfter histopathology, positron emission tomography and computed tomography, or close follow-up, pGGNs were classified to neoplastic and non-neoplastic nodules. There was statistically significant difference regarding the mean size, where in cases of neoplastic nodules it was 6.66 mm and the mean size of benign nodules was 11.25 mm. Moreover, irregularity index and histogram peak frequency had a significant correlation with a P value of 0.007 and 0.022, respectively. The cutoff level for peak frequency percentage was 20.5% with 56.3% sensitivity and 79.2% specificity.ConclusionsRadiomics had a growing role in pulmonary nodule assessment. Radiomics along with morphologic features assessment improved the detection efficiency of neoplastic versus non-neoplastic ground-glass nodules with histogram peak frequency, nodule size, and irregularity index as the main differentiating factors in this study.

Magnetic resonance radiomic feature performance in pulmonary nodule classification and impact of segmentation variability on radiomics.

Investigate the performance of multiparametric MRI radiomic features, alone or combined with current standard-of-care methods, for pulmonary nodule classification. Assess the impact of segmentation variability on feature reproducibility and reliability. Radiomic features were extracted from 74 pulmonary nodules of 68 patients who underwent nodule resection or biopsy after MRI exam. The MRI features were compared with histopathology and conventional quantitative imaging values (maximum standardized uptake value [SUVmax] and mean Hounsfield unit [HU]) to determine whether MRI radiomic features can differentiate types of nodules and associate with SUVmax and HU using Wilcoxon rank sum test and linear regression. Diagnostic performance of features and four machine learning (ML) models were evaluated with area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CIs). Concordance correlation coefficient (CCC) assessed the segmentation variation impact on feature reproducibility and reliability. Elevn diffusion-weighted features distinguished malignant from benign nodules (adjusted p < 0.05, AUC: 0.73-0.81). No features differentiated cancer types. Sixty-seven multiparametric features associated with mean CT HU and 14 correlated with SUVmax. All significant MRI features outperformed traditional imaging parameters (SUVmax, mean HU, apparent diffusion coefficient [ADC], T1, T2, dynamic contrast-enhanced imaging values) in distinguishing malignant from benign nodules with some achieving statistical significance (p < 0.05). Adding ADC and smoking history improved feature performance. Machine learning models demonstrated strong performance in nodule classification, with extreme gradient boosting (XGBoost) having the highest discrimination (AUC = 0.83, CI=[0.727, 0.932]). We found good to excellent inter- and intrareader feature reproducibility and reliability (CCC≥0.80). Eleven MRI radiomic features differentiated malignant from benign lung nodules, outperforming traditional quantitative methods. MRI radiomic ML models demonstrated good nodule classification performances with XGBoost superior to three others. There was good to excellent inter- and intrareader feature reproducibility and reliability. Our study identified MRI radiomic features that successfully differentiated malignant from benign lung nodules and demonstrated high performance of our MR radiomic feature-based ML models for nodule classification. These new findings could help further establish thoracic MRI as a non-invasive and radiation-free alternative to standard practice for pulmonary nodule assessment.

Radiographic assessment of small lung nodules: what can we do and what information does it give us?

: The evaluation of small pulmonary nodules is a common and challenging clinical scenario often requiring a multi-disciplinary approach to optimize patient care. Radiographic assessment of pulmonary nodules is an important part of the determination of whether a nodule is malignant or benign which then translates into an intervention or a management strategy. A multimodality radiological approach or longitudinal follow up may be needed based on a combination of the clinical and imaging features at initial presentation. In this article, we illustrate the role of anatomical and functional radiological techniques [chest radiography, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography/computed tomography (PET/CT)] in the assessment and characterization of small pulmonary nodules, with an emphasis on chest CT, the workhorse of pulmonary imaging. This discussion is framed in the context of how imaging fits into current international guidelines on pulmonary nodule assessment, with an emphasis on both the Fleischner society and British Thoracic Society (BTS) guidelines, including the application of nodule risk prediction scores. Emerging applications including radiomics, machine learning and artificial intelligence (AI) in assessment and characterization of pulmonary nodules and role of these advanced techniques in the management of pulmonary nodules in the future are also illustrated.

Artificial intelligence software in pulmonary nodule assessment.

This study tests the impact of the addition of autonomous computed tomography (CT) interpreting software to radiologist assessment of pulmonary nodules. Computed tomography scans for nodule assessment were identified retrospectively. Lung cancer risk factors, initial radiologist (RAD) report, Philips Lung Nodule software report (computer-aided nodule (CAD)) and radiologist report following the review of CT images and CAD (RAD + CAD) were collected. Follow-up recommendations based on current guidelines were derived from each report. In all, 100 patients were studied. Median maximal diameter of the largest nodule reported by RAD and RAD + CAD were similar at 10.0 and 9.0 mm, respectively (p = 0.06) but were reported as larger by CAD at 11.8 mm (p < 0.001). Follow-up recommendations derived from RAD + CAD were less intensive in 23 (23%) and more intensive in 34 (34%) than that of RAD. This study suggests that autonomous software use can alter radiologist assessment of pulmonary nodules such that suggested follow-up is altered.

MA04.07 A Controlled Study of Pathological T- staging and Imaging T-staging of NSCLC Based on Artificial Intelligence

The incidence of non-small cell lung cancer (NSCLC) currently ranks first in lung cancer, and accurate staging is the key to its prognosis. Multi-slice computed tomography (MSCT) is the preferred method for evaluating lung cancer staging, prognosis and efficacy. By measuring the maximum diameter of the tumor, more accurate preoperative imaging and clinical T staging can be made. In recent years, the rapid development of artificial intelligence (AI) has brought a lot of convenience to physicians in the automatic detection, measurement and risk assessment of pulmonary nodules.

Postoperative computed tomography of insufflated lung specimens obtained by video-assisted thoracic surgery: detection and margin assessment of pulmonary nodules.

ObjectiveTo investigate the utility of computed tomography (CT) scans to detect and assess the margin status of pulmonary nodules that were insufflated after being resected by video-assisted thoracic surgery.Materials and MethodsThis was a novel multicenter study conducted at two national referral centers for thoracic diseases. Patients suspected of having lung cancer underwent video-assisted thoracic surgery for the resection of pulmonary nodules, which were submitted to postoperative CT. Measurements from the CT scans were compared with the results of the histopathological analysis.ResultsA total of 37 pulmonary nodules from 37 patients were evaluated. The mean age of the patients was 65 years (range, 36-84 years), and 27 (73%) were female. A CT analysis of insufflated specimens identified all 37 nodules, and 33 of those nodules were found to have tumor-free margins. The histopathological analysis revealed lung cancer in 30 of the nodules, all with tumor-free margins, and benign lesions in the seven remaining nodules.ConclusionPostoperative CT of insufflated suspicious lung lesions provides real-time detection of pulmonary nodules and satisfactory assessment of tumor margins. This initial study shows that CT of insufflated lung lesions can be a valuable tool at centers where intraoperative histopathological analysis is unavailable.

Intraoperative Lung Ultrasound (ILU) for the Assessment of Pulmonary Nodules.

Background: The primary aim of this study was to confirm the validity of intraoperative lung ultrasound (ILU) as a safe and effective method of localization for difficult to visualize pulmonary nodules during Video-Assisted Thoracoscopic Surgery (VATS) and open thoracotomy. The secondary aim was to enhance knowledge on the morphological patterns of presentation of pulmonary nodules on direct ultrasound examination. Materials and methods: 131 patients with lung nodule and indication for surgery were enrolled. All patients underwent pre-operative imaging of the chest, including Chest Computed Tomography (CT) and Transthoracic Ultrasound (TUS), and surgical procedures for histological assessment of pulmonary nodules (VATS or open thoracotomy). Results: The identification of 100.00% of lung nodules was allowed by ILU, while the detection rate of digital palpation was 94.66%. It was not possible to associate any specific ILU echostructural pattern to both benign or malignant lesions. However, the actual histological margins of the lesions in the operating samples were corresponding to those visualized at ILU in 125/131 (95.42%) cases. No complications have been reported with ILU employment. Conclusions: In our experience, ILU performed during both open surgery and VATS demonstrated to be a reliable and safe method for visualization and localization of pulmonary nodules non previously assessed on digital palpation. In addition, ILU showed to allow a clear nodule’s margins’ definition matching, in most cases, with the actual histological margins.

Role of a portable gamma-camera with optical view for margins assessment of pulmonary nodules resected by radioguided surgery.

Radioguided occult lesion localization (ROLL) of pulmonary nodules is an alternative to hook-wire. Both required of a histological margin assessment. The activity emerging from the radiotracer allows to obtain an intraoperative scintigraphic image of the surgical specimen by a portable gamma-camera (PGC) fitted with an optical view, which provides information about the localization of the nodule in relation to the margins. The aim of this study was to evaluate the intraoperative use of a PGC for margin assessment of pulmonary nodules. ROLL technique was used in 38 nodules (36 pulmonary, 1 chest wall, and 1 pleural nodules). A PGC intraoperative image of the surgical specimen was obtained in 32. Scintigraphic results were compared to the histological assessment. Other factors, such as nodule size, distance from the pleural surface, or distance covered by the needle, were considered as possible factors for non-centered lesions. PGC images showed that the lesion was in contact with the margins in 8/32 cases and centered in 24. In all cases in which the lesion was considered as centered by the PGC, the margins were free of involvement (NPV 100%), although the PPV is low. The use of a PGC for margin assessment after pulmonary nodule resection is feasible and provides a high NPV in our series. In addition, the short intraoperative time required for its use makes the PGC a useful tool for providing supplementary information to histopathologic results. Further studies from different surgical teams are required for an external validation.

Multi-observer concordance and accuracy of the British Thoracic Society scale and other visual assessment qualitative criteria for solid pulmonary nodule assessment using FDG PET-CT

To compare the interobserver reliability and diagnostic accuracy of the British Thoracic Society (BTS) scale and other visual assessment criteria in the context of 2-[18F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET)-computed tomography (CT) evaluation of solid pulmonary nodules (SPNs). Fifty patients who underwent FDG PET-CT for assessment of a SPN were identified. Seven reporters with varied experience at four centres graded FDG uptake visually using the British Thoracic Society (BTS) four-point scale. Five reporters also scored SPNs according to three- and five-point visual assessment scales and using semi-quantitative assessment (maximum standardised uptake value [SUVmax]). Interobserver reliability was assessed with the intra-class correlation coefficient (ICC) and weighted Cohen's kappa (κ). Diagnostic performance was evaluated by receiver operator characteristic (ROC) analysis. Good interobserver reliability was demonstrated with the BTS scale (ICC=0.78, 95% confidence interval [CI]: 0.69-0.85) and five-point scale (ICC=0.78, 95 CI 0.68-0.86), whilst the three-point scale demonstrated moderate reliability (ICC=0.70, 95% CI: 0.59-0.80). Almost perfect agreement was achieved between two consultants (κ=0.85), and substantial agreement between two other consultants (κ=0.78) using the BTS scale. ROC curves for the BTS and five-point scales demonstrated equivalent accuracy (BTS area under the ROC curve [AUC]=0.768; five-point AUC=0.768). SUVmax was no more accurate compared to the BTS scale (SUVmax AUC=0.794; BTS AUC=0.768, p=0.43). The BTS scale can be applied reliably by reporters with varied levels of PET-CT reporting experience, across different centres and has a diagnostic performance that is not surpassed by alternative scales.

Ultra high-resolution computed tomography with 1024-matrix: Comparison with 512-matrix for the evaluation of pulmonary nodules

PurposeTo determine whether a 1024-matrix provides superior image quality for the evaluation of pulmonary nodules. Materials and methodsProspective evaluation conducted between December 2017 and April 2018, during which CT images showing lung nodules of more than 6 mm and less than 30 mmm were reconstructed with 2 different protocols: 0.5-mm thickness, 512 × 512 matrix, 34.5-cm field of view (FOV) (0.5-512 protocol); and 2-mm thickness, 1024 × 1024 matrix, 34.5-cm FOV (2-1024 protocol). Lung nodule characteristics such as margin, lobulation, pleural indentation, spiculation as well as peripheral vessels and bronchioles visibility and overall image quality were evaluated by three chest radiologists, using a 5-point scale. Image noise was evaluated by measuring the standard deviation in the region of interest for each image. ResultsA total of 89 nodules were evaluated. The 2-1024 protocol performed significantly better for the subjective evaluation of pulmonary nodules (p = 0.006 ∼ p < 0.0001). However, image noise was significantly higher both subjectively and objectively (p = 0.036, p < 0.0001). ConclusionThe use of a 2-1024 protocol does not increase the amount of images and allows better assessment of pulmonary nodules, despite noise increase.

The pathological assessment of pulmonary nodules in breast cancer patients by video-assisted thoracoscopic surgery

The pathological assessment of pulmonary nodules in breast cancer patients by video-assisted thoracoscopic surgery

Role of Multi-Slice Computed Tomography Perfusion in Evaluation of the Pulmonary Nodules

The current study aim is the role of multi-slice CT perfusion in assessment of pulmonary nodules. Eighty patients with pulmonary nodules underwent non contrast CT scan of the chest and dynamic CT perfusion of the chest. Dynamic CT chest perfusion of 80 patients with pulmonary nodules revealed 24 patients had benign nodules of low biological activity, 16 patients had benign nodules of high biological activity and 40 patients had malignant nodules (16 of them had multiple nodules in both lung fields and clinical history of primary extra pulmonary malignancy, so diagnosed as metastatic nodules). Perfusion flow, extraction fraction and blood volume; these indexes showed significant differences between malignant nodules and benign nodules with low biologic activity (P>0.0001) In addition, these indexes showed a significant difference between benign nodules with high biologic activity and those with low biologic activity (P>0.0001) and the perfusion flow was of high benefit for nodules characterization than ejection fraction and blood volume by the higher significant values. CT perfusion compared the effect of therapy (chemotherapy and or radiotherapy) on metastatic pulmonary nodules before and after start of treatment by perfusion parameters (perfusion flow, extraction fraction and blood volume) & colour maps with the clinical data and follow up revealed that both results closely near and raise the efficacy of CT perfusion study in follow up and assessment of treatment response in metastatic pulmonary nodules. It can be concluded that CT perfusion is a feasible non-invasive diagnostic technique able to evaluate the nature of pulmonary nodules and treatment response in patients with metastatic pulmonary nodules.

Detection and Classification of Pulmonary Nodules Using Convolutional Neural Networks: A Survey

CT screening has been proven to be effective for diagnosing lung cancer at its early manifestation in the form of pulmonary nodules, thus decreasing the mortality. However, the exponential increase of image data makes their accurate assessment a very challenging task given that the number of radiologists is limited and they have been overworked. Recently, numerous methods, especially ones based on deep learning with convolutional neural network (CNN), have been developed to automatically detect and classify pulmonary nodules in medical images. In this paper, we present a comprehensive analysis of these methods and their performances. First, we briefly introduce the fundamental knowledge of CNN as well as the reasons for their suitability to medical images analysis. Then, a brief description of various medical images datasets, as well as the environmental setup essential for facilitating lung nodule investigations with CNNs, is presented. Furthermore, comprehensive overviews of recent progress in pulmonary nodule analysis using CNNs are provided. Finally, existing challenges and promising directions for further improving the application of CNN to medical images analysis and pulmonary nodule assessment, in particular, are discussed. It is shown that CNNs have transformed greatly the early diagnosis and management of lung cancer. We believe that this review will provide all the medical research communities with the necessary knowledge to master the concept of CNN so as to utilize it for improving the overall human healthcare system.