Detection Of Solitary Pulmonary Nodules Research Articles

A Multi-Modal Machine Learning Methodology for Predicting Solitary Pulmonary Nodule Malignancy in Patients Undergoing PET/CT Examination

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

Intraoperative Contrast-Enhanced Ultrasonography (Io-CEUS) in Minimally Invasive Thoracic Surgery for Characterization of Pulmonary Tumours: A Clinical Feasibility Study.

The intraoperative detection of solitary pulmonary nodules (SPNs) continues to be a major challenge, especially in minimally invasive video-assisted thoracic surgery (VATS). The location, size, and intraoperative frozen section result of SPNs are decisive regarding the extent of lung resection. This feasibility study investigates the technical applicability of intraoperative contrast-enhanced ultrasonography (Io-CEUS) in minimally invasive thoracic surgery. In this prospective, monocentric clinical feasibility study, n = 30 patients who underwent Io-CEUS during elective minimally invasive lung resection for SPNs between October 2021 and February 2023. The primary endpoint was the technical feasibility of Io-CEUS during VATS. Secondary endpoints were defined as the detection and characterization of SPNs. In all patients (female, n = 13; mean age, 63 ± 8.6 years) Io-CEUS could be performed without problems during VATS. All SPNs were detected by Io-CEUS (100%). SPNs had a mean size of 2.2 cm (0.5-4.5 cm) and a mean distance to the lung surface of 2.0 cm (0-6.4 cm). B-mode, colour-coded Doppler sonography, and contrast-enhanced ultrasound were used to characterize all tumours intraoperatively. Significant differences were found, especially in vascularization as well as in contrast agent behaviour, depending on the tumour entity. After successful lung resection, a pathologic examination confirmed the presence of lung carcinomas (n = 17), lung metastases (n = 10), and benign lung tumours (n = 3). The technical feasibility of Io-CEUS was confirmed in VATS before resection regarding the detection of suspicious SPNs. In particular, the use of Doppler sonography and contrast agent kinetics revealed intraoperative specific aspects depending on the tumour entity. Further studies on Io-CEUS and the application of an endoscopic probe for VATS will follow.

Deep Learning-Based Digitally Reconstructed Tomography of the Chest in the Evaluation of Solitary Pulmonary Nodules: A Feasibility Study.

Computed tomography (CT) is preferred for evaluating solitary pulmonary nodules (SPNs) but access or availability may be lacking, in addition, overlapping anatomy can hinder detection of SPNs on chest radiographs. We developed and evaluated the clinical feasibility of a deep learning algorithm to generate digitally reconstructed tomography (DRT) images of the chest from digitally reconstructed frontal and lateral radiographs (DRRs) and use them to detect SPNs. This single-institution retrospective study included 637 patients with noncontrast helical CT of the chest (mean age 68 years, median age 69 years, standard deviation 11.7 years; 355 women) between 11/2012 and 12/2020, with SPNs measuring 10-30 mm. A deep learning model was trained on 562 patients, validated on 60 patients, and tested on the remaining 15 patients. Diagnostic performance (SPN detection) from planar radiography (DRRs and CT scanograms, PR) alone or with DRT was evaluated by two radiologists in an independent blinded fashion. The quality of the DRT SPN image in terms of nodule size and location, morphology, and opacity was also evaluated, and compared to the ground-truth CT images RESULTS: Diagnostic performance was higher from DRT plus PR than from PR alone (area under the receiver operating characteristic curve 0.95-0.98 versus 0.80-0.85; p < 0.05). DRT plus PR enabled diagnosis of SPNs in 11 more patients than PR alone. Interobserver agreement was 0.82 for DRT plus PR and 0.89 for PR alone; and interobserver agreement for size and location, morphology, and opacity of the DRT SPN was 0.94, 0.68, and 0.38, respectively. For SPN detection, DRT plus PR showed better diagnostic performance than PR alone. Deep learning can be used to generate DRT images and improve detection of SPNs.

Improved detection of solitary pulmonary nodules on radiographs compared with deep bone suppression imaging.

The present study aimed to investigate whether deep bone suppression imaging (BSI) could increase the diagnostic performance for solitary pulmonary nodule detection compared with digital tomosynthesis (DTS), dual-energy subtraction (DES) radiography, and conventional chest radiography (CCR). A total of 256 patients (123 with a solitary pulmonary nodule, 133 with normal findings) were included in the study. The confidence score of 6 observers determined the presence or absence of pulmonary nodules in each patient. These were first analyzed using a CCR image, then with CCR plus deep BSI, then with CCR plus DES radiography, and finally with DTS images. Receiver-operating characteristic curves were used to evaluate the performance of the 6 observers in the detection of pulmonary nodules. For the 6 observers, the average area under the curve improved significantly from 0.717 with CCR to 0.848 with CCR plus deep BSI (P<0.01), 0.834 with CCR plus DES radiography (P<0.01), and 0.939 with DTS (P<0.01). Comparisons between CCR and CCR plus deep BSI found that the sensitivities of the assessments by the 3 residents increased from 53.2% to 69.5% (P=0.014) for nodules located in the upper lung field, from 30.6% to 44.6% (P=0.015) for nodules that were partially/completely obscured by the bone, and from 33.2% to 45.8% (P=0.006) for nodules <10 mm. The deep BSI technique can significantly increase the sensitivity of radiology residents for solitary pulmonary nodules compared with CCR. Increased detection was seen mainly for smaller nodules, nodules with partial/complete obscuration, and nodules located in the upper lung field.

Automatic detection of solitary pulmonary nodules using superpixels segmentation based iterative clustering approach

Lung disease is the premier cause of cancer deaths all over the world. The lung cancer diagnosis is mediocre at an early stage since it is impotent by the radiologist. Various investigations conducted so far manifest clearly that the nodule segmentation algorithms are inefficient. Thus, this investigation has centralized superpixels segmentation based iterative clustering (CSSBIC) and a sophisticated optimization approach for detailed segmentation of pulmonary nodules. The supreme intent of this research paper is the enhancement of lung CT images to identify the tumor effectively and small-scale anomalous nodules segmentation in the lung area. The initial phase an-isotropic diffusion with masking (AIDME) enhancement techniques which can eliminate the noise discern in the images. The next step is to apply the centralized superpixels segmentation based iterative clustering (CSSBIC) algorithm by using an improved picture sequence nodule for abnormal lung tissue prediction. The lung nodule is essentially retrieved using a GWO, based on deep learning techniques, with advanced ONN (ONN) and advanced CNN (CNN). The average time for segmenting the Nodule Slice order is 1.06 s. The best classification accuracy is 97% via GWO based advanced one nearest neighbour (AONN) and 97.6% by GWO based advanced convolutional neural network (ACNN) classifier.

DNA methylation of PTGER4 in peripheral blood plasma helps to distinguish between lung cancer, benign pulmonary nodules and chronic obstructive pulmonary disease patients

Background/introductionIn contrast to patients who present with advanced stage lung cancer and associated poor prognosis, patients with early-stage lung cancer may be candidates for curative treatments. The results of the NELSON lung cancer screening trial are expected to stimulate the development and implementation of a lung cancer screening strategy in most countries. Widespread use of chest computed tomography scans will also result in the detection of solitary pulmonary nodules. Because reliable biomarkers to distinguish between malignant and benign lesions are lacking, tissue-based histopathological diagnostics remain the gold standard.In this study, we aimed to establish a test to assess the predictive ability of DNA hypermethylation of SHOX2 and PTGER4 in plasma to discriminate between patients with 1.) lung cancer, 2.) benign lesions, and 3.) patients with chronic obstructive pulmonary disease (COPD). Patients and methodsWe retrospectively analysed SHOX2 and PTGER4 methylation in 121 prospectively collected plasma samples of patients with lung cancer (group 1A), benign lesions (group 1B), and COPD without nodules (group 2). ResultsPTGER4 DNA hypermethylation was more frequently observed in patients with lung cancer than in controls (p = 0.0004). Results remained significant after correction for tumour volume, smoking status, age, and eligibility for the NELSON trial. ConclusionsDetection of methylated PTGER4 in plasma DNA may serve as a biomarker to support clinical decision-making in patients with pulmonary lesions at lung cancer screening in high-risk populations. Further exploration in prospective studies is warranted.

Detection of Solitary Pulmonary Nodules Based on Brain-Computer Interface.

Solitary pulmonary nodules are the main manifestation of pulmonary lesions. Doctors often make diagnosis by observing the lung CT images. In order to further study the brain response structure and construct a brain-computer interface, we propose an isolated pulmonary nodule detection model based on a brain-computer interface. First, a single channel time-frequency feature extraction model is constructed based on the analysis of EEG data. Second, a multilayer fusion model is proposed to establish the brain-computer interface by connecting the brain electrical signal with a computer. Finally, according to image presentation, a three-frame image presentation method with different window widths and window positions is proposed to effectively detect the solitary pulmonary nodules.

Differences in the clinical management of women and men after detection of a solitary pulmonary nodule in clinical practice.

To explore differences in the clinical management of men and women in the 5years after detecting a solitary pulmonary nodule (SPN) by chest radiograph or CT in routine clinical practice. We followed up 545 men and 347 women with an SPN detected by chest radiograph or CT in a retrospective cohort of 25,422 individuals undergoing routine thoracic imaging in 2010-2011. We compared the frequency of each management strategy (no further test, immediate intervention or follow up) according to sex by means of chi-squared. We estimated the relative risk of women versus men of having been followed up instead of an immediate intervention using multivariate logistic regression. We compared by sex the time between detection of the nodule and lung cancer diagnosis, the time between diagnosis and death by means of Mann-Whitney U test and the cumulative effective dose of radiation in each management strategy by means of t test. Women were more likely than men to have follow-up rather than immediate intervention (aRR = 1.8, CI 1.3-2.7, p = 0.002), particularly in those who underwent CT (aRR = 4.2, CI 1.9-9.3, p < 0.001). The median time between SPN detection and lung cancer diagnosis was higher in women (4.2months, interquartile range (IQR) 5.1) than in men (1.5months, IQR 16.2). The mean cumulative effective dose was 21.3mSv, 19.4mSv in men and 23.9mv in women (p = 0.023). Our results could reflect decisions based on a greater suspicion of lung cancer in men. The incidental detection of SPNs is increasing, and it is necessary to establish clear strategies aimed to reduce variability in their management according to patient's sex. • After incidental finding of SPN, women were less likely to receive an immediate intervention. • Accumulative radiation was higher in women than in men. • Our results could reflect decisions based on a greater suspicion of lung cancer in men.

A Novel Algorithm for Segmentation of Solitary Pulmonary Nodules in Chest Computed Tomography Based on Three-Dimensional Connected Voxels

Background: With the increasing incidence of lung cancer, it is prudent to do screenings for individuals at high-risk. Solitary pulmonary nodules (SPNs) are an indication of small tumors or early stages of disease. Therefore, accurate detection of SPNs is important to both clinicians and radiologists. Since a large number of computed tomography (CT) scans are being acquired during a lung cancer screening, there is an urgent need for new automated techniques to detect SPNs. Methods: A novel algorithm for segmentation of SPNs in CT scans based on three-dimensional connected voxels (3DCVs) can be used to screen out potential patients with SPNs. 120 cases of CT scans from a public database (100 positive cases with nodules and 20 negative cases without nodules) and 30 negative cases from the routine CT scans completed in a hospital were used to test the algorithm. The algorithm is based on the fact that most pulmonary nodules are solitary at their early stages. First, find suitable CT values thresholds for CT values to convert pulmonary nodules, normal tissues and air spaces in each chest CT slice into black and white images. Then stack the slices in their originally physical order. This will produce a three-dimensional (3D) matrix with pulmonary nodules and normal tissues constructing their own 3DCVs respectively. Results: Of the 100 positive cases, 93 cases showed positive detection of SPNs and 7 cases did not. Of the 50 negative cases, 48 cases returned a negative result and 2 cases showed as positive result. In this study, the sensitivity is 93% and the specificity is 96% with a 4% false positive rate (FPR). Conclusions: This algorithm can be used to screen out positive chest CT scans efficiently, which will increase efficiency by two to three times than when compared with manual inspection and detection.

The determinants of lung cancer after detecting a solitary pulmonary nodule are different in men and women, for both chest radiograph and CT.

ObjectivesTo determine the factors associated with lung cancer diagnosis and mortality after detecting a solitary pulmonary nodule (SPN) in routine clinical practice, in men and in women for both chest radiograph and CT.Materials and methodsA 5-year follow-up of a retrospective cohort of of 25,422 (12,594 men, 12,827 women) patients aged ≥35 years referred for chest radiograph or CT in two hospitals in Spain (2010–2011). SPN were detected in 893 (546 men, 347 women) patients. We estimated the cumulative incidence of lung cancer at 5-years, the association of patient and nodule characteristics with SPN malignancy using Poisson logistic regression, stratifying by sex and type of imaging test. We calculated lung cancer specific mortality rate by sex and SPN detection and hazard rates by cox regression.Results133 (14.9%) out of 893 patients with an SPN and 505 (2.06%) of the 24,529 patients without SPN were diagnosed with lung cancer. Median diameter of SPN in women who developed cancer was larger than in men. Men who had a chest radiograph were more likely to develop a lung cancer if the nodule was in the upper-lobes, which was not the case for women. In patients with an SPN, smoking increased the risk of lung cancer among men (chest radiograph: RR = 11.3, 95%CI 1.5–83.3; CT: RR = 7.5, 95%CI 2.2, 26.0) but smoking was not significantly associated with lung cancer diagnosis or mortality among women with an SPN. The relative risk of lung cancer diagnosis in women with SPN versus those without was much higher compared to men (13.7; 95%CI 9.2, 20.4 versus 6.2; 95%CI 4.9,7.9).ConclusionThe factors associated with SPN malignancy and 5-year lung cancer mortality were different among men and women, especially regarding smoking history and SPN characteristics, where we observed a relatively high rate of lung cancer diagnosis among female non-smokers.

Treatment of pulmonary nodule: from VATS to RATS.

The incidental detection of solitary pulmonary nodule (SPN) is currently increasing due to the widespread use of computed tomography (CT) during the follow up in oncological patients or in screening trials. A quick and definitive histological diagnosis of these nodes is mandatory as, in case of primitive lung cancer, an early detection could improve both surgical results and prognosis. The minimally invasive pulmonary resection (MIPR) is the gold standard procedure for diagnosis and treatment of small lung nodules, but it can be difficult to localize deep nonpalpable nodes that lie in the lung parenchyma. Hence, throughout the years several techniques have been developed to better localize deep or sub solid nodes. We describe our experience with radio-guided technique. Patients with SPN smaller than 10 mm and/or with a distance from the visceral pleura equal or larger than 10 mm underwent MIPR after CT injection of a solution containing 0.2 mL 99Tc-labelled human serum albumin microspheres and 0.1 mL of non-ionic contrast. During surgical procedures, a collimated probe, connected to a gamma-ray detector, was used to scan the lung and detect the target area. The area of major radioactivity was then resected. Between 2010 and 2015, 175 patients (M/F: 97/78) underwent minimally invasive resection (thoracoscopic or robotic) with radio-guided technique. The mean node diameter was 13 mm (range, 5-20 mm), and the mean distance from the visceral pleura was 15 mm (range, 6-39 mm). No significant CT-guided-injection-related complications occurred, except for 13 patients (7.4%) who developed a pneumothorax (PNX) not requiring chest tube insertion. This technique guaranteed a successfully intra-operative node localization in 100% of cases. The mean duration of the surgical procedure was 44 min (range, 25-130 min). The mean length of pleural drainage and mean hospital stay was 2.6 days (range, 1-5 days) and 3.9 days (range, 1-7 days) respectively. No mortality or perioperative complications occurred. Pathology reports showed 105 metastases, 55 primitive lung cancers and 15 benign lesions. In case of suspicion of primitive lung tumour an intraoperative frozen exam was conducted, and all patients underwent lobectomy or segmentectomy with lymphadenectomy [41 patients with thoracotomy, 11 with robotic surgery and 3 with video-assisted thoracoscopic surgery (VATS)]. Our experience confirms that radio-guided thoracoscopic surgery (RGTS) is a feasible and safe procedure, thanks to its high success rate the radio-guided technique is helpful to overcome the lack of tactile feedback during MIPR [VATS and robotic-assisted thoracoscopic surgery (RATS)] and to limit conversion to open surgery.

Standardized uptake value and radiological density attenuation as predictive and prognostic factors in patients with solitary pulmonary nodules: our experience on 1,592 patients.

Multislice computed tomography (MSCT) increased detection of solitary pulmonary nodules (SPNs), changing the management based on radiological and clinical factors. When 18-fluorine fluorodeoxyglucose positron emission tomography combined with computed tomography (18F-FDG-PET/CT) was considered for the evaluation of nodules, the maximum standardized uptake value (SUVmax) more than 2.5 is used frequently as a cut off for malignancy. The purpose of this study is to evaluate SUVmax PET/CT and pulmonary attenuation patterns at MSCT in patients with SPN according to morphological and pathological characteristics of the lesion. A retrospective study on 1,592 SPN patients was carried out following approval by the Italian Registry of VATS Lobectomies. All patients underwent VATS lobectomy. On histologic examination, 98.1% had primary or second metachronous primary lung cancers. In addition, 10.7% presented occult lymph node metastases (pN1 or pN2) on histological examination. Nodule attenuation on CT was associated with the histology of the lesion (p= 0.030); in particular, pure ground glass opacities (pGGOs) and partially solid nodules were related to adenocarcinomatous histotypes. Conversely, a significant relationship between SUVmax and age, nodule size, pathological node status (pN) was found (P=0.007, P=0.000 and P=0.002 respectively). Nodule attenuation can predict the histology of the lesion whereas SUVmax may relate to the propensity to lymph node metastases.

Diagnosis of Solitary Pulmonary Nodules Using MRI: A Feasibility Study

Solitary pulmonary nodule (SPN) is explained as a separate, well-determined obscurity with the maximum size of 3cm which is enclosed by typical lung tissue and is not related to any other anomalies in the lung or adjacent lymph nodes. Among imaging modalities MRI has more benefits such as high spatial resolution and usage of nonionizing radiation, however, the efficacy of this imaging modality in lung nodules diagnosis has not been well established. Thus CT scan is considered a proper modality for the SPN diagnosis. The objective of this study was to evaluate the accuracy of MRI in the detection of SPNs which has verified by CT scan. The study was conducted as a prospective case-control study. 32 SPN patients whose disease confirmed by CT scan findings and 11 patient with normal CT scan were included in this study. Sensitivity, specificity, positive and negative predictive values of MRI were determined. The location and the size of pulmonary nodules as well as different MRI sequences were evaluated. Statistical analysis revealed that T1w sequences (27%) had the least diagnostic sensitivity, T2w images, and Fat-sat slices had 55% and 36% sensitivity respectively. Specificity of T1w, T2w, and Fat-sat images were 97%, 90% for the last two imaging sequences. According to the results, it can be concluded that although the specificity of different MRI sequences was ? 90%, low sensitivity and breath holding technique prevents its clinical routine usage for the suspected patients with SPN or early detection of pulmonary nodules in high-risk patients.

P1.03-078 Size Matters...but Don't Underestimate the Power of Morphology

The detection of solitary pulmonary nodules has increased due to the widespread use of Computed Tomography (CT) and will increase even more in the future when lung cancer screening will be embedded in daily practice. In addition to the clinical information, size is one of the most important parameters to assess the likelihood of malignancy. Although there is a considerable overlap in imaging characteristics of benign and malignant solitary pulmonary nodules, the power of morphology –even in small nodules- should not be underestimated. The aim of this pictorial essay is to give an overview of the wide range of morphologic characteristics and to address the available evidence on sensitivity and specificity of these characteristics. Cases presented were collected during the Multidisciplinary Thoracic Oncology Tumor Board between January 2014 and May 2016. All malignant cases have histopathologic proof, whereas benign lesions were included when the benign nature was suggested after follow-up, negative PET-scan and/or multidisciplinary consensus. With regard to margin characteristics, spiculation is highly suggestive of a malignant nature. It is the only feature that is incorporated as ‘morphologic’ variable in most risk prediction models. Other features however may also be strong indicators of malignancy. Lobulation, halo sign, pleural indentation, vascular convergence sign and pitfall sign are frequently encountered in malignant nodules. The nodule-bronchus relationship can give additional information regarding the nature of the nodule, with signs such as air bronchogram, bubble like lucencies and bronchus cutoff sign being indicative of a malignant nature. In cavitated nodules, a very thin wall might indicate a benign cause, whereas a very thick wall is more common in malignant nodules. Calcification is typically seen in benign nodules, but depending on the calcification pattern a malignant etiology cannot be excluded. The presence of fat is a relative reliable sign of benignity. Thin-section CT will enable detection of subtle findings. Nodules rarely present with only one characteristic and combination of findings can definitely increase the likelihood of a correct diagnosis. The management of solitary pulmonary nodules involves both clinical and imaging assessment. Although a great overlap exists in morphologic features of benign and malignant nodules, thorough knowledge and recognition of subtle morphologic findings will aid in early detection of nodules with a high likelihood of malignancy and will avoid unnecessary follow-up and delay in diagnosis and treatment.

Automatic detection of solitary pulmonary nodules using swarm intelligence optimized neural networks on CT images

Lung Cancer is one of the most dangerous diseases that cause a large number of deaths. Early detection and analysis will be the only remedy. Computer-Aided Diagnosis (CAD) plays a key role in the early detection and diagnosis of lung cancer. This paper develops a CAD system that focus on new heuristic search algorithm to optimize the Back Propagation Neural Network (BPNN) in characterizing nodule from non-nodules. The proposed CAD system consists of four main stages: (i) image acquisition (ii) lesion detection, (iii) texture feature extraction and (iv) tumor characterization using a classifier. The optimization mechanism employs Particle Swarm Optimization (PSO) with new inertia weight for NN in order to investigate the classification rate of these algorithms in reducing the problems of trapping in local minima and the slow convergence rate of current evolutionary learning algorithms. The experiments were conducted on CT images to classify into nodule and non-nodule from the tumor region of interest. The performance of the CAD system was evaluated for the texture characterized images taken from LIDC-IDRI and SPIE-AAPM databases. Due to improved inertia weight used in Particle Swarm (PS) the CAD achieves highest classification accuracy of 98% for solid nodules, 99.5% for part solid nodules and 97.2% for non solid nodules respectively. The experimental results suggest that the developed CAD system has great potential and promise in the automatic diagnosis of tumors of lung.

Comparison of Positron Emission Tomography Using 2-[18F]-fluoro-2-deoxy-D-glucose and 3-deoxy-3-[18F]-fluorothymidine in Lung Cancer Imaging.

Background:The detection of solitary pulmonary nodules (SPNs) that may potentially develop into a malignant lesion is essential for early clinical interventions. However, grading classification based on computed tomography (CT) imaging results remains a significant challenge. The 2-[18F]-fluoro-2-deoxy-D-glucose (18F-FDG) positron emission tomography (PET)/CT imaging produces both false-positive and false-negative findings for the diagnosis of SPNs. In this study, we compared 18F-FDG and 3-deoxy-3-[18F]-fluorothymidine (18F-FLT) in lung cancer PET/CT imaging.Methods:The binding ratios of the two tracers to A549 lung cancer cells were calculated. The mouse lung cancer model was established (n = 12), and micro-PET/CT analysis using the two tracers was performed. Images using the two tracers were collected from 55 lung cancer patients with SPNs. The correlation among the cell-tracer binding ratios, standardized uptake values (SUVs), and Ki-67 proliferation marker expression were investigated.Results:The cell-tracer binding ratio for the A549 cells using the 18F-FDG was greater than the ratio using 18F-FLT (P < 0.05). The Ki-67 expression showed a significant positive correlation with the 18F-FLT binding ratio (r = 0.824, P < 0.01). The tumor-to-nontumor uptake ratio of 18F-FDG imaging in xenografts was higher than that of 18F-FLT imaging. The diagnostic sensitivity, specificity, and the accuracy of 18F-FDG for lung cancer were 89%, 67%, and 73%, respectively. Moreover, the diagnostic sensitivity, specificity, and the accuracy of 18F-FLT for lung cancer were 71%, 79%, and 76%, respectively. There was an obvious positive correlation between the lung cancer Ki-67 expression and the mean maximum SUV of 18F-FDG and 18F-FLT (r = 0.658, P < 0.05 and r = 0.724, P < 0.01, respectively).Conclusions:The 18F-FDG uptake ratio is higher than that of 18F-FLT in A549 cells at the cellular level. 18F-FLT imaging might be superior for the quantitative diagnosis of lung tumor tissue and could distinguish lung cancer nodules from other SPNs.

Contribution of nonattenuation-corrected images on FDG-PET/CT in the assessment of solitary pulmonary nodules.

In this study, we aim to determine the diagnostic performance of nonattenuation-corrected (NAC) and attenuation-corrected (AC) FDG-PET/CT images in the assessment of solitary pulmonary nodule (SPN). We reviewed the images of 41 patients who underwent FDG-PET/CT to diagnose SPNs. The visual analysis of FDG uptake intensity in SPN on AC and NAC PET images was made using a four-point score from 1 to 4 on both AC and NAC PET images. The cutoff value of SUVmax and visual uptake scores for malignancy were defined as≥2.5 and≥3, respectively. The significant visual uptake (≥2 visual point score) on AC and NAC PET images was considered to be positive 18F-FDG PET findings for lesion detectability. The sensitivity, specificity and diagnostic accuracy were calculated for AC and NAC PET images. Based on the histopathology and imaging data, 22 of the SPNs (54%) were malignant and 19 of them (46%) were benign. The sensitivity and NPV were found to be 100% in the detection of SPNs for AC and NAC PET images. For all SPNs and SPNs≤2cm, NAC PET image had a higher diagnostic performance for the SPN characterization as malignant or benign, when compared with AC PET image. The success rates of AC and NAC PET images were found to be similar for the detection of SPNs. NAC PET image had a higher diagnostic performance for the SPN characterization. It is thought that NAC PET image may provide additional contributions for characterization of SPNs.

Comparison of the accuracy of 99mTc-3P4-RGD2 SPECT and CT in diagnosing solitary pulmonary nodules.

The aim of the present study was to compare technetium-99m-(polyethylene glycol-4)3-(Arg-Gly-Asp)2 (99mTc-3P4-RGD2) single-photon emission computed tomography (SPECT) and computed tomography (CT) in the noninvasive differentiation of solitary pulmonary nodules (SPNs). The present study prospectively investigated a consecutive series of 24 patients with SPN, who were newly diagnosed using radiography between September 2012 and January 2014. All patients underwent 99mTc-3P4-RGD2 SPECT and CT scans using a dual-head variable-angle γ-camera equipped with high-resolution collimators. A blinded panel of two thoracic radiologists for CT and three nuclear physicians for SPECT analyzed the images using a 5-grade scale. The SPECT images were also semi-quantitatively evaluated using tumor to non-tumor localization ratios (T/NT). The results were verified by pathological examination of the biopsy material obtained from each patient with SPN, and receiver operating characteristic (ROC) curves were generated from these results. The present results revealed that there were 17 malignant and 7 benign SPNs among the 24 patients with SPN. The mean size of the SPN was 2.1±0.6 cm. Sensitivity of visual analysis for SPECT and CT were 100.0 and 82.4%, respectively, and specificity was 71.4% for the two methods. When the T/NT SPECT semiquantitative analysis (ratio, 1.64) was used as a cut-off, the sensitivity and specificity of SPECT were 100.0 and 71.4%, respectively. The areas under the ROC curves were 0.840 for visual analysis of SPECT [95% confidence interval (CI), 0.600–1.000], 0.849 for semiquantitative analysis of SPECT (95% CI, 0.618–1.000) and 0.815 for CT (95% CI, 0.626–1.000). In conclusion, the present results suggest that 99mTc-3P4-RGD2 SPECT is more accurate than CT in the detection of malignant SPN, and visual analysis appears to be sufficient for the characterization of SPN.

The Fate of Patients with Solitary Pulmonary Nodules: Clinical Management and Radiation Exposure Associated.

BackgroundThe appropriate management of the large number of lung nodules detected during the course of routine medical care presents a challenge. We aimed to evaluate the usual clinical practice in solitary pulmonary nodule (SPN) management and associated radiation exposure.MethodsWe examined 893 radiology reports of consecutive patients undergoing chest computed tomography (CT) and radiography at two public hospitals in Spain. Information on diagnostic procedures from SPN detection and lung cancer diagnosis was collected prospectively for 18 months.ResultsMore than 20% of patients with SPN detected on either chest radiograph (19.8%) or CT (26.1%) underwent no additional interventions and none developed lung cancer (100% negative predictive value). 346 (72.0%) patients with SPN detected on chest radiograph and 254 (61.5%) patients with SPN detected on CT had additional diagnostic tests and were not diagnosed with lung cancer. In patients undergoing follow-up imaging for SPNs detected on CT median number of additional imaging tests was 3.5 and the mean cumulative effective dose was 24.4 mSv; for those detected on chest radiograph the median number of additional imaging tests was 2.8 and the mean cumulative effective dose was 10.3 mSv.ConclusionsPatients who did not have additional interventions were not diagnosed of lung cancer. There was an excessive amount of interventions in a high percentage of patients presenting SPN, which was associated with an excess of radiation exposure.

LROC Investigation of Three Strategies for Reducing the Impact of Respiratory Motion on the Detection of Solitary Pulmonary Nodules in SPECT.

The objective of this investigation was to determine the effectiveness of three motion reducing strategies in diminishing the degrading impact of respiratory motion on the detection of small solitary pulmonary nodules (SPN) in single photon emission computed tomographic (SPECT) imaging in comparison to a standard clinical acquisition and the ideal case of imaging in the absence of respiratory motion. To do this non-uniform rational B-spline cardiac-torso (NCAT) phantoms based on human-volunteer CT studies were generated spanning the respiratory cycle for a normal background distribution of Tc-99m NeoTect. Similarly, spherical phantoms of 1.0 cm diameter were generated to model small SPN for each of 150 uniquely located sites within the lungs whose respiratory motion was based on the motion of normal structures in the volunteer CT studies. The SIMIND Monte Carlo program was used to produce SPECT projection data from these. Normal and single-lesion containing SPECT projection sets with a clinically realistic Poisson noise level were created for the cases of: 1) the end-expiration (EE) frame with all counts, 2) respiration-averaged motion with all counts, 3) one-fourth of the 32 frames centered around EE (Quarter-Binning), 4) one-half of the 32 frames centered around EE (Half-Binning), and 5) eight temporally binned frames spanning the respiratory cycle. Each of the sets of combined projection data were reconstructed with RBI-EM with system spatial-resolution compensation (RC). Based on the known motion for each of the 150 different lesions, the reconstructed volumes of respiratory bins were shifted so as to superimpose the locations of the SPN onto that in the first bin (Reconstruct and Shift). Five human-observers performed localization receiver operating characteristics (LROC) studies of SPN detection. The observer results were analyzed for statistical significance differences in SPN detection accuracy among the three correction strategies, the standard acquisition, and the ideal case of the absence of respiratory motion. Our human-observer LROC determined that Quarter-Binning and Half-Binning strategies resulted in SPN detection accuracy statistically significantly below (P < 0.05) that of standard clinical acquisition, whereas the Reconstruct and Shift strategy resulted in a detection accuracy not statistically significantly different from that of the ideal case. This investigation demonstrates that tumor detection based on acquisitions associated with less than all the counts which could potentially be employed may result in poorer detection despite limiting the motion of the lesion. The Reconstruct and Shift method results in tumor detection that is equivalent to ideal motion correction.