Evidence based imaging strategies for solitary pulmonary nodule

Comparative study of the effect of preoperative hookwire and methylene blue localization techniques on post-operative hospital stay and complications in thoracoscopic pulmonary nodule surgery

BACKGROUND

Direct localization of small and deep pulmonary nodules before thoracoscopic surgery using the hookwire or methylene blue techniques has been recently attempted for better surgical outcomes. In this study, we compare the outcomes of the above two techniques.

METHODS

Two hundred and nineteen patients undergoing 135 hookwire and 151 methylene blue techniques in our University Hospital between July 2020 and January 2022 were compared for localization and hospitalization durations, and the complication risk. Other confounders included patients' age, gender, localization position, nodules location, count, diameter, and depth.

RESULTS

After adjustment of all predictors, the methylene blue technique was associated with a significant 0.6-min (parameter estimate (PE) = -0.568, p value = 0.0173) and an 0.7-day shorter localization and hospitalization time (PE = -0.713, p value =  < 0.0001) as compared to using the hookwire technique. The hookwire technique was significantly associated with 5 times the risk of developing a post-localization complication (Adjusted Odds Ratio (Adj OR) = 4.52, 95% CI 1.53-13.33) and 3.6 times the risk of developing a pneumothorax (Adj OR = 3.57, 95% CI 1.1-11.62) as compared to adopting the methylene blue technique.

CONCLUSIONS

Compared to the hook wire technique, the methylene blue technique offers a shorter procedure and hospitalization stay, as well as a safer post-operative experience.

Solitary Pulmonary Inflammatory Nodule: CT Features and Pathological Findings

Purpose

Solitary pulmonary inflammatory nodules (SPINs) are frequently misdiagnosed as malignancy. We aimed to investigate CT features and pathological findings of SPINs for improving diagnosis strategies.

Patients and Methods

In this retrospective study, 225 and 310 consecutive patients with confirmed SPINs and lung cancerous nodules were enrolled from January 2013 to December 2020. Nodules were classified into different types based on the key CT features: I, homogeneous and well-defined nodules with smooth (Ia), coarse (Ib), or spiculated margins (Ic); II, nodules with blurred boundaries, peripheral patches, or both; III, nodules exhibiting heterogeneous density; and IV, polygonal nodules. The pathological findings of SPINs were simultaneously studied and summarized.

Results

Among the 225 SPINs, type I (Ia, Ib, and Ic), II, III, and IV were 137 (60.9%) (47 [20.9%], 33 [14.7%], and 57 [25.3%]), 62 (27.6%), 12 (5.3%) and 14 (6.2%), respectively. Correspondingly, those in 310 cancerous nodules were 275 (88.7%) (119 [38.4%], 70 [22.6%], and 86 [27.7%]), 20 (6.5%), 15 (4.8%), and 0, respectively. Compared with lung cancers, type I nodules were less common but type II and IV nodules were more common in SPINs (each P < 0.0001). Though the frequencies of subtype I (P = 0.095) and type III (P = 0.796) nodules were similar between two groups, their specific CT features were significantly different. The main pathological findings of each type of SPINs were most extensively identical (82.2 - 100%).

Conclusion

Between cancerous nodules and SPINs, differences in overall or specific CT features exist. The type II and IV nodules are highly indicative of SPINs, and each type of SPINs have almost similar pathological findings.

Spiculation Sign Recognition in a Pulmonary Nodule Based on Spiking Neural P Systems

The spiculation sign is one of the main signs to distinguish benign and malignant pulmonary nodules. In order to effectively extract the image feature of a pulmonary nodule for the spiculation sign distinguishment, a new spiculation sign recognition model is proposed based on the doctors' diagnosis process of pulmonary nodules. A maximum density projection model is established to fuse the local three-dimensional information into the two-dimensional image. The complete boundary of a pulmonary nodule is extracted by the improved Snake model, which can take full advantage of the parallel calculation of the Spike Neural P Systems to build a new neural network structure. In this paper, our experiments show that the proposed algorithm can accurately extract the boundary of a pulmonary nodule and effectively improve the recognition rate of the spiculation sign.

Optical Flow Methods for Lung Nodule Segmentation on LIDC-IDRI Images

Lung nodule segmentation is an essential step in any CAD system for lung cancer detection and diagnosis. Traditional approaches for image segmentation are mainly morphology based or intensity based. Motion-based segmentation techniques tend to use the temporal information along with the morphology and intensity information to perform segmentation of regions of interest in videos. CT scans comprise of a sequence of dicom 2-D image slices similar to videos which also comprise of a sequence of image frames ordered on a timeline. In this work, Farneback, Horn-Schunck and Lucas-Kanade optical flow methods have been used for processing the dicom slices. The novelty of this work lies in the usage of optical flow methods, generally used in motion-based segmentation tasks, for the segmentation of nodules from CT images. Since thin-sliced CT scans are the imaging modality considered, they closely approximate the motion videos and are the primary motivation for using optical flow for lung nodule segmentation. This paper also provides a detailed comparative analysis and validates the effectiveness of using optical flow methods for segmentation. Finally, we propose methods to further improve the efficiency of segmentation using optical flow methods on CT scans.

Magnetic resonance imaging of adrenal gland: state of the art

Detection of adrenal lesions, because of the widespread use of imaging and especially high-resolution imaging procedures, is increased. Because of the importance to characterize those findings, magnetic resonance imaging (MRI), in particular chemical shift imaging (CSI), is useful to distinguish whether a lesion is benignant or malignant and to avoid further diagnostic or surgical procedures. It represents the first choice of imaging in patient like children or pregnant women, and a valid complement to other imaging techniques like CT or PET/CT. In this review we analyze the role and characteristic of MRI and the imaging features of most common benignant (adenoma, hyperplasia, pheochromocytoma, hemorrhage, cyst, myelolipoma, teratoma, ganglioneuroma, cystic lymphangioma, hemangioma) and malignant [neuroblastoma, adrenocortical carcinoma (ACC), metastases, lymphoma] adrenal lesions.

Consistency of radiologists in identifying pulmonary nodules based on low-dose computed tomography

Background

To study the consistency of radiologists in identifying pulmonary nodules based on low-dose computed tomography (LDCT), and to analyze factors that affect the consistency.

Methods

A total of 750 LDCT cases were collected randomly from three medical centers. Three experienced chest radiologists independently evaluated and detected the pulmonary nodules on 625 cases of LDCT images. The detected nodules were classified into 3 groups: group I (detected by all radiologists); group II (detected by two radiologists); group III (detected by only one radiologist). The consistency with respect to the image features of individual nodules was assessed.

Results

A total of 1,206 nodules were identified by the three radiologists. There were 234 (19.4%) nodules in group I, 377 (31.3%) nodules in group II, and 595 (49.3%) nodules in group III. Logistic regression showed that the size, density, and location of the nodules correlated with the detection of nodules. Nodules sized great than or equal to 4 mm were more consistently identified than nodules sized less than 4 mm. Solid and calcified nodules were more consistently identified than sub-solid nodules. Nodules located in the outer zone were more consistently identified than hilar nodules.

Conclusions

There was considerable inter-reader variability with respect to identification of pulmonary nodules in LDCT. Larger nodules, solid or calcified nodules, and nodules located in the outer zone were more consistently identified.

CT and MRI of adrenal gland pathologies

Besides ultrasound and nuclear medicine techniques, computed tomography (CT) and magnetic resonance imaging (MRI) are commonly used to examine adrenal lesions in both symptomatic and asymptomatic patients. Some adrenal lesions have characteristic radiological features. If an adrenal nodule is discovered incidentally, determining whether the lesion is benign or malignant is of great importance. According to their biological behavior, lesions can be divided into benign (mainly: adenoma, hyperplasia, pheochromocytoma, cyst, hemorrhage, cystic lymphangioma, myelolipoma, hemangioma, ganglioneuroma, teratoma) and malignant (mainly: metastases, adrenal cortical carcinoma, neuroblastoma, lymphoma) conditions. In this paper, we review CT/MRI findings of common adrenal gland lesions.

Characterization of Pulmonary Nodules Based on Features of Margin Sharpness and Texture

Lung cancer is the leading cause of cancer-related deaths in the world, and one of its manifestations occurs with the appearance of pulmonary nodules. The classification of pulmonary nodules may be a complex task to specialists due to temporal, subjective, and qualitative aspects. Therefore, it is important to integrate computational tools to the early pulmonary nodule classification process, since they have the potential to characterize objectively and quantitatively the lesions. In this context, the goal of this work is to perform the classification of pulmonary nodules based on image features of texture and margin sharpness. Computed tomography scans were obtained from a publicly available image database. Texture attributes were extracted from a co-occurrence matrix obtained from the nodule volume. Margin sharpness attributes were extracted from perpendicular lines drawn over the borders on all nodule slices. Feature selection was performed by different algorithms. Classification was performed by several machine learning classifiers and assessed by the area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy. Highest classification performance was obtained by a random forest algorithm with all 48 extracted features. However, a decision tree using only two selected features obtained statistically equivalent performance on sensitivity and specificity.

Image-based management of empiric lung stereotactic body radiotherapy (SBRT) without biopsy: Predictors from a 10-year single institution experience

Background

There is emerging reliance on clinical imaging for the diagnosis, prognosis, and treatment evaluation of early stage non‐small cell lung cancer (NSCLC) in patients deemed too high risk for biopsy. We report our experience of clinically diagnosed NSCLC treated empirically with stereotactic body radiotherapy (SBRT) to validate the imaging parameters used for management in this high‐risk population.

Methods

We reviewed 101 empiric lung SBRT cases and profiled imaging specifics of computed tomography and positron emission tomography for diagnosis and follow‐up. Secondarily, we identified potential correlates of disease progression with Cox regression multivariate analysis.

Results

Fifty‐seven men and 43 women aged 45–94 (median 76) were treated with a median dose of 48 Gy in four fractions. The median nodule diameter was 1.6 cm (0.6–4.5 cm) and most were spiculated (n = 58), right‐sided (n = 63), and in the upper lobe (n = 68). Median follow‐up and survival rates were 14 and 28 months, respectively. Local control at three years was 94%. Freedom from any progression at one and three years was 85% and 69%, respectively. Toxicity ≥ grade 3 included two grade 3 dyspneas. A pre‐treatment standard uptake value > 4.1 was the only significant predictor of disease progression.

Conclusion

This study illustrates the instrumental role of modern clinical imaging for the effective management of presumed early stage NSCLC treated with empiric lung SBRT. As lung SBRT without tissue confirmation becomes more common, hopefully these assertions can be prospectively validated.

Logistic regression analysis and a risk prediction model of pneumothorax after CT-guided needle biopsy

Background

Pneumothorax is the most common complication of computed tomography (CT)-guided needle biopsy. The purpose of this study was to investigate independent risk factors of pneumothorax, other than emphysema, after CT-guided needle biopsy and to establish a risk prediction model.

Methods

A total of 864 cases of CT-guided needle biopsy with an 18-gauge cutting needle were enrolled in this study. The relevant risk factors associated with pneumothorax included age, sex, emphysema, short-axis size of the lesion, depth of the lesion, body position, and the number of pleural punctures. Several independent risk factors of pneumothorax were found, and a predictive model for pneumothorax was established using univariate and multivariate logistic regression analyses.

Results

Pneumothorax occurred in 31.4% (271/864) of cases. Univariate analysis showed that significant risk factors of pneumothorax included age, emphysema, small lesion size, no contact between the lesion and the pleura, prone or lateral body position, and multiple punctures. Independent risk factors of pneumothorax in the multivariate logistic regression analysis included emphysema (P=0.000), no contact between the lesion and the pleura (P=0.000), prone or lateral body position (P=0.002), and the number of pleural punctures (P=0.000). The sensitivity, specificity, and accuracy of the predictive model for pneumothorax were 56.8%, 79.6%, and 72.5%, respectively.

Conclusions

Pneumothorax is a common complication of CT-guided lung biopsy. Independent risk factors of pneumothorax include emphysema, no contact between the lesion and the pleura, and prone or lateral body position. The predictive model developed in this study was highly accurate in predicting the incidence of pneumothorax.

The impact of chemotherapy on persistent ground-glass nodules in patients with lung adenocarcinoma

Backgrounds

To evaluate the response of persistent ground glass nodules (GGNs) in patients with lung adenocarcinoma treated with platinum-based chemotherapy on computed tomography (CT).

Methods

We retrospectively studied patients with GGNs that met the following criteria: (I) GGNs found in patients with lung adenocarcinoma, which persist for more than 3 months; (II) patients treated with platinum-based (cisplatin or carboplatin) chemotherapy for at least 2 cycles; (III) ground glass proportion ¡Ý50%. For each patient, if more than two CTs satisfied the inclusion criteria, then the baseline and last CTs were used for analysis, defined as CT1 and CT2. A total of 91 persistent pulmonary GGNs in 51 patients fulfilled the inclusion criteria. We defined growth as a nodule ¡Ý2 mm increase in diameter or showing up a solid portion. GGN response to therapy was assessed and compared with the baseline CT. Differences in CT findings were analyzed using a paired t-test and Pearson ¦Ö² test.

Results

Between 2010 and 2015, 25 of the 51 (49%) were male and 26 of the 51 (51%) were female. The average age at time of detection of a GGN was 63.8 (range, 36-84) years. Mean follow-up duration was 24.1¡À17.9 months. During the follow-up periods, on a per-nodule basis, 94.5% of GGNs (n=86) remained unchanged in size. Only 5.5% GGNs (n=5) in 5 patients increased in size. The nodules CT feature in each lung adenocarcinoma clinical stage show no difference. No significant difference was found in the size, attenuation, volume, and mass of GGN between baseline and post-treatment measurements, regardless of the type of chemotherapy (P>0.05).

Conclusions

The clinical course of GGNs in patients with lung adenocarcinoma is predominantly indolent, and platinum-based chemotherapy may have no effect on the growth of persistent GGNs.

Enhanced characterization of solid solitary pulmonary nodules with Bayesian analysis-based computer-aided diagnosis

The aim of this study was to prospectively assess the accuracy gain of Bayesian analysis-based computer-aided diagnosis (CAD) vs human judgment alone in characterizing solitary pulmonary nodules (SPNs) at computed tomography (CT). The study included 100 randomly selected SPNs with a definitive diagnosis. Nodule features at first and follow-up CT scans as well as clinical data were evaluated individually on a 1 to 5 points risk chart by 7 radiologists, firstly blinded then aware of Bayesian Inference Malignancy Calculator (BIMC) model predictions. Raters’ predictions were evaluated by means of receiver operating characteristic (ROC) curve analysis and decision analysis. Overall ROC area under the curve was 0.758 before and 0.803 after the disclosure of CAD predictions (P = 0.003). A net gain in diagnostic accuracy was found in 6 out of 7 readers. Mean risk class of benign nodules dropped from 2.48 to 2.29, while mean risk class of malignancies rose from 3.66 to 3.92. Awareness of CAD predictions also determined a significant drop on mean indeterminate SPNs (15 vs 23.86 SPNs) and raised the mean number of correct and confident diagnoses (mean 39.57 vs 25.71 SPNs). This study provides evidence supporting the integration of the Bayesian analysis-based BIMC model in SPN characterization.

Solid pulmonary nodule risk assessment and decision analysis: comparison of four prediction models in 285 cases

OBJECTIVES

The aim of this study was to compare classification results from four major risk prediction models in a wide population of incidentally detected solitary pulmonary nodules (SPNs) which were selected to crossmatch inclusion criteria for the selected models.

METHODS

A total of 285 solitary pulmonary nodules with a definitive diagnosis were evaluated by means of four major risk assessment models developed from non-screening populations, namely the Mayo, Gurney, PKUPH and BIMC models. Accuracy was evaluated by receiver operating characteristic (ROC) area under the curve (AUC) analysis. Each model's fitness to provide reliable help in decision analysis was primarily assessed by adopting a surgical threshold of 65 % and an observation threshold of 5 % as suggested by ACCP guidelines.

RESULTS

ROC AUC values, false positives, false negatives and indeterminate nodules were respectively 0.775, 3, 8, 227 (Mayo); 0.794, 41, 6, 125 (Gurney); 0.889, 42, 0, 144 (PKUPH); 0.898, 16, 0, 118 (BIMC).

CONCLUSIONS

Resultant data suggests that the BIMC model may be of greater help than Mayo, Gurney and PKUPH models in preoperative SPN characterization when using ACCP risk thresholds because of overall better accuracy and smaller numbers of indeterminate nodules and false positive results.

KEY POINTS

• The BIMC and PKUPH models offer better characterization than older prediction models • Both the PKUPH and BIMC models completely avoided false negative results • The Mayo model suffers from a large number of indeterminate results.

Magnetic resonance imaging for lung cancer screen

Lung cancer is the leading cause of cancer related death throughout the world. Lung cancer is an example of a disease for which a large percentage of the high-risk population can be easily identified via a smoking history. This has led to the investigation of lung cancer screening with low-dose helical/multi-detector CT. Evidences suggest that early detection of lung cancer allow more timely therapeutic intervention and thus a more favorable prognosis for the patient. The positive relationship of lesion size to likelihood of malignancy has been demonstrated previously, at least 99% of all nodules 4 mm or smaller are benign, while noncalcified nodules larger than 8 mm diameter bear a substantial risk of malignancy. In the recent years, the availability of high-performance gradient systems, in conjunction with phased-array receiver coils and optimized imaging sequences, has made MR imaging of the lung feasible. It can now be assumed a threshold size of 3-4 mm for detection of lung nodules with MRI under the optimal conditions of successful breath-holds with reliable gating or triggering. In these conditions, 90% of all 3-mm nodules can be correctly diagnosed and that nodules 5 mm and larger are detected with 100% sensitivity. Parallel imaging can significantly shorten the imaging acquisition time by utilizing the diversity of sensitivity profile of individual coil elements in multi-channel radiofrequency receive coil arrays or transmit/receive coil arrays to reduce the number of phase encoding steps required in imaging procedure. Compressed sensing technique accelerates imaging acquisition from dramatically undersampled data set by exploiting the sparsity of the images in an appropriate transform domain. With the combined imaging algorithm of parallel imaging and compressed sensing and advanced 32-channel or 64-channel RF hardware, overall imaging acceleration of 20 folds or higher can then be expected, ultimately achieve free-breathing and no ECG gating acquisitions in lung cancer MRI screening. Further development of protocols, more clinical trials and the use of advanced analysis tools will further evaluate the real significance of lung MRI.