New classification of small pulmonary nodules by margin characteristics on high-resolution CT

Development of automatic generation system for lung nodule finding descriptions

Worldwide, lung cancer is the leading cause of cancer-related deaths. To manage lung nodules, radiologists observe computed tomography images, review various imaging findings, and record these in radiology reports. The report contents should be of high quality and uniform regardless of the radiologist. Here, we propose an artificial intelligence system that automatically generates descriptions related to lung nodules in computed tomography images. Our system consists of an image recognition method for extracting contents-namely, bronchopulmonary segments and nodule characteristics from images-and a natural language processing method to generate fluent descriptions. To verify our system's clinical usefulness, we conducted an experiment in which two radiologists created nodule descriptions of findings using our system. Through our system, the similarity of the described contents between the two radiologists (p = 0.001) and the comprehensiveness of the contents (p = 0.025) improved, while the accuracy did not significantly deteriorate (p = 0.484).

Construction and validation of a predictive model of invasive adenocarcinoma in pure ground-glass nodules less than 2 cm in diameter

OBJECTIVES

In this study, we aimed to develop a multiparameter prediction model to improve the diagnostic accuracy of invasive adenocarcinoma in pulmonary pure glass nodules.

METHOD

We included patients with pulmonary pure glass nodules who underwent lung resection and had a clear pathology between January 2020 and January 2022 at the Qilu Hospital of Shandong University. We collected data on the clinical characteristics of the patients as well as their preoperative biomarker results and computed tomography features. Thereafter, we performed univariate and multivariate logistic regression analyses to identify independent risk factors, which were then used to develop a prediction model and nomogram. We then evaluated the recognition ability of the model via receiver operating characteristic (ROC) curve analysis and assessed its calibration ability using the Hosmer-Lemeshow test and calibration curves. Further, to assess the clinical utility of the nomogram, we performed decision curve analysis.

RESULT

We included 563 patients, comprising 174 and 389 cases of invasive and non-invasive adenocarcinoma, respectively, and identified seven independent risk factors, namely, maximum tumor diameter, age, serum amyloid level, pleural effusion sign, bronchial sign, tumor location, and lobulation. The area under the ROC curve was 0.839 (95% CI: 0.798-0.879) for the training cohort and 0.782 (95% CI: 0.706-0.858) for the validation cohort, indicating a relatively high predictive accuracy for the nomogram. Calibration curves for the prediction model also showed good calibration for both cohorts, and decision curve analysis showed that the clinical prediction model has clinical utility.

CONCLUSION

The novel nomogram thus constructed for identifying invasive adenocarcinoma in patients with isolated pulmonary pure glass nodules exhibited excellent discriminatory power, calibration capacity, and clinical utility.

LIONS PREY: A New Logistic Scoring System for the Prediction of Malignant Pulmonary Nodules

OBJECTIVES

Classifying radiologic pulmonary lesions as malignant is challenging. Scoring systems like the Mayo model lack precision in predicting the probability of malignancy. We developed the logistic scoring system 'LIONS PREY' (Lung lesION Score PREdicts malignancY), which is superior to existing models in its precision in determining the likelihood of malignancy.

METHODS

We evaluated all patients that were presented to our multidisciplinary team between January 2013 and December 2020. Availability of pathological results after resection or CT-/EBUS-guided sampling was mandatory for study inclusion. Two groups were formed: Group A (malignant nodule; n = 238) and Group B (benign nodule; n = 148). Initially, 22 potential score parameters were derived from the patients' medical histories.

RESULTS

After uni- and multivariate analysis, we identified the following eight parameters that were integrated into a scoring system: (1) age (Group A: 64.5 ± 10.2 years vs. Group B: 61.6 ± 13.8 years; multivariate p-value: 0.054); (2) nodule size (21.8 ± 7.5 mm vs. 18.3 ± 7.9 mm; p = 0.051); (3) spiculation (73.1% vs. 41.9%; p = 0.024); (4) solidity (84.9% vs. 62.8%; p = 0.004); (5) size dynamics (6.4 ± 7.7 mm/3 months vs. 0.2 ± 0.9 mm/3 months; p < 0.0001); (6) smoking history (92.0% vs. 43.9%; p < 0.0001); (7) pack years (35.1 ± 19.1 vs. 21.3 ± 18.8; p = 0.079); and (8) cancer history (34.9% vs. 24.3%; p = 0.052). Our model demonstrated superior precision to that of the Mayo score (p = 0.013) with an overall correct classification of 96.0%, a calibration (observed/expected-ratio) of 1.1, and a discrimination (ROC analysis) of AUC (95% CI) 0.94 (0.92-0.97).

CONCLUSIONS

Focusing on essential parameters, LIONS PREY can be easily and reproducibly applied based on computed tomography (CT) scans. Multidisciplinary team members could use it to facilitate decision making. Patients may find it easier to consent to surgery knowing the likelihood of pulmonary malignancy. The LIONS PREY app is available for free on Android and iOS devices.

Discrimination tasks in simulated low-dose CT noise

BACKGROUND

This study reports the results of a set of discrimination experiments using simulated images that represent the appearance of subtle lesions in low-dose computed tomography (CT) of the lungs. Noise in these images has a characteristic ramp-spectrum before apodization by noise control filters. We consider three specific diagnostic features that determine whether a lesion is considered malignant or benign, two system-resolution levels, and four apodization levels for a total of 24 experimental conditions.

PURPOSE

The goal of the investigation is to better understand how well human observers perform subtle discrimination tasks like these, and the mechanisms of that performance. We use a forced-choice psychophysical paradigm to estimate observer efficiency and classification images. These measures quantify how effectively subjects can read the images, and how they use images to perform discrimination tasks across the different imaging conditions.

MATERIALS AND METHODS

The simulated CT images used as stimuli in the psychophysical experiments are generated from high-resolution objects passed through a modulation transfer function (MTF) before down-sampling to the image-pixel grid. Acquisition noise is then added with a ramp noise-power spectrum (NPS), with subsequent smoothing through apodization filters. The features considered are lesion size, indistinct lesion boundary, and a nonuniform lesion interior. System resolution is implemented by an MTF with resolution (10% max.) of 0.47 or 0.58 cyc/mm. Apodization is implemented by a Shepp-Logan filter (Sinc profile) with various cutoffs. Six medically naïve subjects participated in the psychophysical studies, entailing training and testing components for each condition. Training consisted of staircase procedures to find the 80% correct threshold for each subject, and testing involved 2000 psychophysical trials at the threshold value for each subject. Human-observer performance is compared to the Ideal Observer to generate estimates of task efficiency. The significance of imaging factors is assessed using ANOVA. Classification images are used to estimate the linear template weights used by subjects to perform these tasks. Classification-image spectra are used to analyze subject weights in the spatial-frequency domain.

RESULTS

Overall, average observer efficiency is relatively low in these experiments (10%-40%) relative to detection and localization studies reported previously. We find significant effects for feature type and apodization level on observer efficiency. Somewhat surprisingly, system resolution is not a significant factor. Efficiency effects of the different features appear to be well explained by the profile of the linear templates in the classification images. Increasingly strong apodization is found to both increase the classification-image weights and to increase the mean-frequency of the classification-image spectra. A secondary analysis of "Unapodized" classification images shows that this is largely due to observers undoing (inverting) the effects of apodization filters.

CONCLUSIONS

These studies demonstrate that human observers can be relatively inefficient at feature-discrimination tasks in ramp-spectrum noise. Observers appear to be adapting to frequency suppression implemented in apodization filters, but there are residual effects that are not explained by spatial weighting patterns. The studies also suggest that the mechanisms for improving performance through the application of noise-control filters may require further investigation.

Diagnostic ability of deep learning in detection of pancreatic tumour

Pancreatic cancer is associated with higher mortality rates due to insufficient diagnosis techniques, often diagnosed at an advanced stage when effective treatment is no longer possible. Therefore, automated systems that can detect cancer early are crucial to improve diagnosis and treatment outcomes. In the medical field, several algorithms have been put into use. Valid and interpretable data are essential for effective diagnosis and therapy. There is much room for cutting-edge computer systems to develop. The main objective of this research is to predict pancreatic cancer early using deep learning and metaheuristic techniques. This research aims to create a deep learning and metaheuristic techniques-based system to predict pancreatic cancer early by analyzing medical imaging data, mainly CT scans, and identifying vital features and cancerous growths in the pancreas using Convolutional Neural Network (CNN) and YOLO model-based CNN (YCNN) models. Once diagnosed, the disease cannot be effectively treated, and its progression is unpredictable. That's why there's been a push in recent years to implement fully automated systems that can sense cancer at a prior stage and improve diagnosis and treatment. The paper aims to evaluate the effectiveness of the novel YCNN approach compared to other modern methods in predicting pancreatic cancer. To predict the vital features from the CT scan and the proportion of cancer feasts in the pancreas using the threshold parameters booked as markers. This paper employs a deep learning approach called a Convolutional Neural network (CNN) model to predict pancreatic cancer images. In addition, we use the YOLO model-based CNN (YCNN) to aid in the categorization process. Both biomarkers and CT image dataset is used for testing. The YCNN method was shown to perform well by a cent percent of accuracy compared to other modern techniques in a thorough review of comparative findings.

Spontaneous transient size reduction of a solitary pulmonary metastasis from a leiomyosarcoma

BACKGROUND

A solitary pulmonary nodule (SPN) poses a diagnostic challenge, which includes both a benign and malignant etiology. A size enlargement often indicates malignancy. We herein describe a case of a solitary pulmonary metastasis from a leiomyosarcoma that regressed transiently during follow-up.

CASE PRESENTATION

A 47-year-old woman presented with an SPN detected by follow-up computed tomography 7 years after surgery for a left forearm high-grade leiomyosarcoma. The nodule regressed spontaneously after an additional 6 months, and therefore, an inflammatory change was the most likely diagnosis at that time. However, the nodule enlarged again over the next 5 years. The growth rate led us to suspect a malignancy. A trans-bronchial biopsy was undiagnostic and a video-assisted thoracic surgery was planned. She underwent a wedge resection of the right lung, and a histopathological examination found it was a metastatic leiomyosarcoma.

CONCLUSIONS

A pulmonary metastasis from a leiomyosarcoma could emerge as an SPN and reveal a subsequent transient size reduction. An SPN in patients even with a remote history of a soft tissue tumor should raise the possibility of metastasis, and periodic follow-up is essential even after the size reduction.

Using Occlusion-Based Saliency Maps to Explain an Artificial Intelligence Tool in Lung Cancer Screening: Agreement Between Radiologists, Labels, and Visual Prompts

Occlusion-based saliency maps (OBSMs) are one of the approaches for interpreting decision-making process of an artificial intelligence (AI) system. This study explores the agreement among text responses from a cohort of radiologists to describe diagnostically relevant areas on low-dose CT (LDCT) images. It also explores if radiologists' descriptions of cases misclassified by the AI provide a rationale for ruling out the AI's output. The OBSM indicating the importance of different pixels on the final decision made by an AI were generated for 10 benign cases (3 misclassified by the AI tool as malignant) and 10 malignant cases (2 misclassified by the AI tool as benign). Thirty-six radiologists were asked to use radiological vocabulary, typical to reporting LDCT scans, to describe the mapped regions of interest (ROI). The radiologists' annotations were then grouped by using a clustering-based technique. Topics were extracted from the annotations and for each ROI, a percentage of annotations containing each topic were found. Radiologists annotated 17 and 24 unique ROIs on benign and malignant cases, respectively. Agreement on the main label (e.g., "vessel," "nodule") by radiologists was only seen in only in 12% of all areas (5/41 ROI). Topic analyses identified six descriptors which are commonly associated with a lower malignancy likelihood. Eight common topics related to a higher malignancy likelihood were also determined. Occlusion-based saliency maps were used to explain an AI decision-making process to radiologists, who in turn have provided insight into the level of agreement between the AI's decision and radiological lexicon.

Predictors of Invasive Adenocarcinomas among Pure Ground-Glass Nodules Less Than 2 cm in Diameter

Simple Summary

Benign lesions, atypical adenomatous hyperplasia, and malignancies such as adenocarcinoma in situ, minimally invasive adenocarcinoma, and invasive adenocarcinoma may feature pure ground-glass nodules on chest CT images, and the prognosis of patients with invasive adenocarcinoma is worse than others. The early detection and adequate management of invasive adenocarcinoma is crucial, but the pathology diagnosis of small nodules is difficult to obtain without surgery. Our study aimed to analyze the CT characteristics of pure ground-glass nodules <2 cm for the identification of invasive adenocarcinomas. A total of 181 nodules in 171 patients were enrolled. The larger size, lobulation, and air cavity were significantly more common in invasive adenocarcinoma. The air cavity is the significant predictor in multivariate analysis. In conclusion, the possibility of invasive adenocarcinoma is higher in a pure ground-glass nodules when it is associated with a larger size, lobulation, and air cavity.

Abstract

Benign lesions, atypical adenomatous hyperplasia (AAH), and malignancies such as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IA) may feature a pure ground-glass nodule (pGGN) on a thin-slide computed tomography (CT) image. According to the World Health Organization (WHO) classification for lung cancer, the prognosis of patients with IA is worse than those with AIS and MIA. It is relatively risky to perform a core needle biopsy of a pGGN less than 2 cm to obtain a reliable pathological diagnosis. The early and adequate management of patients with IA may provide a favorable prognosis. This study aimed to disclose suggestive signs of CT to accurately predict IA among the pGGNs. A total of 181 pGGNs of less than 2 cm, in 171 patients who had preoperative CT-guided localization for surgical excision of a lung nodule between December 2013 and August 2019, were enrolled. All had CT images of 0.625 mm slice thickness during CT-guided intervention to confirm that the nodules were purely ground glass. The clinical data, CT images, and pathological reports of those 171 patients were reviewed. The CT findings of pGGNs including the location, the maximal diameter in the long axis (size-L), the maximal short axis diameter perpendicular to the size-L (size-S), and the mean value of long and short axis diameters (size-M), internal content, shape, interface, margin, lobulation, spiculation, air cavity, vessel relationship, and pleural retraction were recorded and analyzed. The final pathological diagnoses of the 181 pGGNs comprised 29 benign nodules, 14 AAHs, 25 AISs, 55 MIAs, and 58 IAs. Statistical analysis showed that there were significant differences among the aforementioned five groups with respect to size-L, size-S, and size-M (p = 0.029, 0.043, 0.025, respectively). In the univariate analysis, there were significant differences between the invasive adenocarcinomas and the non-invasive adenocarcinomas with respect to the size-L, size-S, size-M, lobulation, and air cavity (p = 0.009, 0.016, 0.008, 0.031, 0.004, respectively) between the invasive adenocarcinomas and the non-invasive adenocarcinomas. The receiver operating characteristic (ROC) curve of size for discriminating invasive adenocarcinoma also revealed similar area under curve (AUC) values among size-L (0.620), size-S (0.614), and size-M (0.623). The cut-off value of 7 mm in size-M had a sensitivity of 50.0% and a specificity of 76.4% for detecting IAs. In the multivariate analysis, the presence of air cavity was a significant predictor of IA (p = 0.042). In conclusion, the possibility of IA is higher in a pGGN when it is associated with a larger size, lobulation, and air cavity. The air cavity is the significant predictor of IA.

Unexpected exposure to Mycobacterium tuberculosis during bronchoscopy using radial probe endobronchial ultrasound

BACKGROUND

Bronchoscopy using radial probe endobronchial ultrasound (EBUS) is performed when a peripheral lung lesion (PLL) is suspected to be malignant. However, pulmonary tuberculosis is diagnosed in some patients, and healthcare workers could therefore be exposed to tuberculosis if sufficient precautions are not taken. In this study, we examined the proportion of and factors associated with unexpected exposure to Mycobacterium tuberculosis during bronchoscopy using radial probe EBUS.

METHODS

This retrospective study included 970 patients who received bronchoscopy using radial probe EBUS between December 2015 and November 2018. Clinical, histological, radiological, and microbiological data were reviewed.

RESULTS

Pulmonary tuberculosis was diagnosed in 31 patients (3.2%) during bronchoscopy using radial probe EBUS. Patients with a lower age were significantly more likely to be diagnosed with tuberculosis than elderly patients (odds ratio [OR], 0.951; 95% confidence interval [CI], 0.924-0.978; P = 0.001). Among the various CT findings, a low HUs difference between pre- and post-enhanced CT (OR, 0.976; 95% CI, 0.955-0.996; P = 0.022), the presence of concentric cavitation (OR, 5.211; 95% CI, 1.447-18.759; P = 0.012), and the presence of satellite centrilobular nodules (OR, 22.925; 95% CI, 10.556-49.785; P < 0.001) were independently associated with diagnosis of tuberculosis.

CONCLUSIONS

The proportion of unexpected exposure to Mycobacterium tuberculosis during bronchoscopy using radial probe EBUS was 3.2%. A higher risk was independently associated with a younger age and CT findings of a small difference in HUs between pre- and post-enhancement images, concentric cavitation, and the presence of a satellite centrilobular nodule.

Hybrid models for lung nodule malignancy prediction utilizing convolutional neural network ensembles and clinical data

Purpose: Due to the high incidence and mortality rates of lung cancer worldwide, early detection of a precancerous lesion is essential. Low-dose computed tomography is a commonly used technique for screening, diagnosis, and prognosis of non-small-cell lung cancer. Recently, convolutional neural networks (CNN) had shown great potential in lung nodule classification. Clinical information (family history, gender, and smoking history) together with nodule size provide information about lung cancer risk. Large nodules have greater risk than small nodules. Approach: A subset of cases from the National Lung Screening Trial was chosen as a dataset in our study. We divided the nodules into large and small nodules based on different clinical guideline thresholds and then analyzed the groups individually. Similarly, we also analyzed clinical features by dividing them into groups. CNNs were designed and trained over each of these groups individually. To our knowledge, this is the first study to incorporate nodule size and clinical features for classification using CNN. We further made a hybrid model using an ensemble with the CNN models of clinical and size information to enhance malignancy prediction. Results: From our study, we obtained 0.9 AUC and 83.12% accuracy, which was a significant improvement over our previous best results. Conclusions: In conclusion, we found that dividing the nodules by size and clinical information for building predictive models resulted in improved malignancy predictions. Our analysis also showed that appropriately integrating clinical information and size groups could further improve risk prediction.

Management of incidental pulmonary nodules: current strategies and future perspectives

Introduction: Detection and characterization of pulmonary nodules is an important issue, because the process is the first step in the management of lung cancers.Areas covered: Literature review was performed on May 15 2019 by using the PubMed, US National Library of Medicine National Institutes of Health, and the National Center for Biotechnology information. CT features helping identify the druggable mutations and predict the prognosis of malignant nodules were presented. Technical advancements in MRI and PET/CT were introduced for providing functional information about malignant nodules. Advances in various tissue biopsy techniques enabling molecular analysis and histologic diagnosis of indeterminate nodules were also presented. New techniques such as radiomics, deep learning (DL) technology, and artificial intelligence showing promise in differentiating between malignant and benign nodules were summarized. Recently, updated management guidelines for solid and subsolid nodules incidentally detected on CT were described. Risk stratification and prediction models for indeterminate nodules under active investigation were briefly summarized.Expert opinion: Advancement in CT knowledge has led to a better correlation between CT features and genomic alterations or tumor histology. Recent advances like PET/CT, MRI, radiomics, and DL-based approach have shown promising results in the characterization and prognostication of pulmonary nodules.

Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches

Risk stratification (characterization) of tumors from radiology images can be more accurate and faster with computer-aided diagnosis (CAD) tools. Tumor characterization through such tools can also enable non-invasive cancer staging, prognosis, and foster personalized treatment planning as a part of precision medicine. In this papet, we propose both supervised and unsupervised machine learning strategies to improve tumor characterization. Our first approach is based on supervised learning for which we demonstrate significant gains with deep learning algorithms, particularly by utilizing a 3D convolutional neural network and transfer learning. Motivated by the radiologists' interpretations of the scans, we then show how to incorporate task-dependent feature representations into a CAD system via a graph-regularized sparse multi-task learning framework. In the second approach, we explore an unsupervised learning algorithm to address the limited availability of labeled training data, a common problem in medical imaging applications. Inspired by learning from label proportion approaches in computer vision, we propose to use proportion-support vector machine for characterizing tumors. We also seek the answer to the fundamental question about the goodness of "deep features" for unsupervised tumor classification. We evaluate our proposed supervised and unsupervised learning algorithms on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans, respectively, and obtain the state-of-the-art sensitivity and specificity results in both problems.

A Novel Computer-Aided Diagnosis Scheme on Small Annotated Set: G2C-CAD

Purpose

Computer-aided diagnosis (CAD) can aid in improving diagnostic level; however, the main problem currently faced by CAD is that it cannot obtain sufficient labeled samples. To solve this problem, in this study, we adopt a generative adversarial network (GAN) approach and design a semisupervised learning algorithm, named G2C-CAD.

Methods

From the National Cancer Institute (NCI) Lung Image Database Consortium (LIDC) dataset, we extracted four types of pulmonary nodule sign images closely related to lung cancer: noncentral calcification, lobulation, spiculation, and nonsolid/ground-glass opacity (GGO) texture, obtaining a total of 3,196 samples. In addition, we randomly selected 2,000 non-lesion image blocks as negative samples. We split the data 90% for training and 10% for testing. We designed a DCGAN generative adversarial framework and trained it on the small sample set. We also trained our designed CNN-based fuzzy Co-forest on the labeled small sample set and obtained a preliminary classifier. Then, coupled with the simulated unlabeled samples generated by the trained DCGAN, we conducted iterative semisupervised learning, which continually improved the classification performance of the fuzzy Co-forest until the termination condition was reached. Finally, we tested the fuzzy Co-forest and compared its performance with that of a C4.5 random decision forest and the G2C-CAD system without the fuzzy scheme, using ROC and confusion matrix for evaluation.

Results

Four different types of lung cancer-related signs were used in the classification experiment: noncentral calcification, lobulation, spiculation, and nonsolid/ground-glass opacity (GGO) texture, along with negative image samples. For these five classes, the G2C-CAD system obtained AUCs of 0.946, 0.912, 0.908, 0.887, and 0.939, respectively. The average accuracy of G2C-CAD exceeded that of the C4.5 random decision tree by 14%. G2C-CAD also obtained promising test results on the LISS signs dataset; its AUCs for GGO, lobulation, spiculation, pleural indentation, and negative image samples were 0.972, 0.964, 0.941, 0.967, and 0.953, respectively.

Conclusion

The experimental results show that G2C-CAD is an appropriate method for addressing the problem of insufficient labeled samples in the medical image analysis field. Moreover, our system can be used to establish a training sample library for CAD classification diagnosis, which is important for future medical image analysis.

Evaluation of a preclinical photon-counting CT prototype for pulmonary imaging

The purpose of this study was to investigate a preclinical spectral photon-counting CT (SPCCT) prototype compared to conventional CT for pulmonary imaging. A custom-made lung phantom, including nodules of different sizes and shapes, was scanned with a preclinical SPCCT and a conventional CT in standard and high-resolution (HR-CT) mode. Volume estimation was evaluated by linear regression. Shape similarity was evaluated with the Dice similarity coefficient. Spatial resolution was investigated via MTF for each imaging system. In-vivo rabbit lung images from the SPCCT system were subjectively reviewed. Evaluating the volume estimation, linear regression showed best results for the SPCCT compared to CT and HR-CT with a root mean squared error of 21.3 mm³, 28.5 mm³ and 26.4 mm³ for SPCCT, CT and HR-CT, respectively. The Dice similarity coefficient was superior for SPCCT throughout nodule shapes and all nodule sizes (mean, SPCCT: 0.90; CT: 0.85; HR-CT: 0.85). 10% MTF improved from 10.1 LP/cm for HR-CT to 21.7 LP/cm for SPCCT. Visual investigation of small pulmonary structures was superior for SPCCT in the animal study. In conclusion, the SPCCT prototype has the potential to improve the assessment of lung structures due to higher resolution compared to conventional CT.

Solitary Pulmonary Lesion in Patients with History of Malignancy: Primary Lung Cancer or Metastatic Cancer?

BACKGROUND

Defining the status of solitary pulmonary lesion (SPL) in patients with history of malignancy is important because primary lung cancer (PLC) or intrapulmonary metastasis might indicate different surgical strategies. The aim of this study is to identify factors related to the status of these lesions and construct a clinical model to estimate the pretest probability of PLC.

METHODS

From January 2005 to January 2016, 104 patients with previous malignancy and suitable for surgery were retrospectively studied. Univariate and multivariate analyses were performed to identify possible factors related to SPLs. A nomogram was constructed to differentiate PLC from intrapulmonary metastasis.

RESULTS

Ninety-seven (93.3%) patients were diagnosed as malignant postoperatively, including 61 patients with intrapulmonary metastasis and 36 patients with PLC. Multivariate analysis showed that site of primary tumor [head and neck squamous cell cancer: odds ratio (OR) = 28.509, P = 0.006; genitourinary cancer: OR = 23.928, P = 0.012], negative lymph node status of primary tumor (OR = 3.154, P = 0.038), spiculation of SPL (OR = 3.972, P = 0.022), and central location of SPL (OR = 4.679, P = 0.026) were four independent factors differentiating PLC from intrapulmonary metastasis. All of these were included in the nomogram. The C-index of the nomogram for predicting probability was 0.82.

CONCLUSIONS

Incidence of malignant SPLs was fairly high in patients with history of malignancy. A nomogram including site and lymph node status of primary tumor, and spiculation and location of SPL might be a good tool for differentiating PLC from intrapulmonary metastasis preoperatively and guiding treatment.

What CT characteristics of lepidic predominant pattern lung adenocarcinomas correlate with invasiveness on pathology?

OBJECTIVES

The International Association for the Study of Lung Cancer, American Thoracic Society and European Respiratory Society lung adenocarcinoma classification in 2011 defined three lepidic predominant patterns including adenocarcinoma in situ, minimally invasive adenocarcinoma and lepidic predominant adenocarcinoma. We sought to correlate the radiology and pathology findings and identify any computed tomography (CT) features which can be associated with invasive growth.

MATERIALS AND METHODS

An institutional review board approved, retrospective study was conducted evaluating 63 patients with resected, pathologically confirmed, adenocarcinomas with predominant lepidic patterns. Preoperative CT images of the nodules were assessed using quantitative and qualitative radiographic descriptors while blinded to pathologic sub-classification and size. Maximum diameter was measured after evaluation of the axial, sagittal and coronal planes. Radiologic - pathologic associations were examined using Fisher's exact test, the Kruskal-Wallis test and the Spearman correlation coefficient (ρ).

RESULTS AND CONCLUSION

Increasing maximum diameter of the whole lesion (ground glass and solid component) on CT was significantly associated with invasiveness (p = .003), as was the maximum pathologic specimen diameter (p = .008). Larger diameter of the solid component on CT was also found in lepidic predominant adenocarcinoma compared to minimally invasive adenocarcinoma (median 10.5 vs 2 mm, p = .005). More invasive tumors had higher visual estimated percentage solid component compared to whole lesion measurement on CT (p = .014). CT and pathologic measurements were positively correlated, although only moderately (ρ = .66) for the maximum whole lesion size and fair (ρ = .49) for solid/invasive component maximum measurements. Larger whole lesion size and solid component size of lepidic predominant pattern adenocarcinomas are associated with lesion invasiveness, although radiologic and pathologic lesion measurements are only fair-moderately positively correlated.

Radiologic Features of Small Pulmonary Nodules and Lung Cancer Risk in the National Lung Screening Trial: A Nested Case-Control Study

Purpose To extract radiologic features from small pulmonary nodules (SPNs) that did not meet the original criteria for a positive screening test and identify features associated with lung cancer risk by using data and images from the National Lung Screening Trial (NLST). Materials and Methods Radiologic features in SPNs in baseline low-dose computed tomography (CT) screening studies that did not meet NLST criteria to be considered a positive screening examination were extracted. SPNs were identified for 73 incident case patients who were given a diagnosis of lung cancer at either the first or second follow-up screening study and for 157 control subjects who had undergone three consecutive negative screening studies. Multivariable logistic regression was used to assess the association between radiologic features and lung cancer risk. All statistical tests were two sided. Results Nine features were significantly different between case patients and control subjects. Backward elimination followed by bootstrap resampling identified a reduced model of highly informative radiologic features with an area under the receiver operating characteristic curve of 0.932 (95% confidence interval [CI]: 0.88, 0.96), a specificity of 92.38% (95% CI: 52.22%, 84.91%), and a sensitivity of 76.55% (95% CI: 87.50%, 95.35%) that included total emphysema score (odds ratio [OR] = 1.71; 95% CI: 1.39, 2.01), attachment to vessel (OR = 2.41; 95% CI: 0.99, 5.81), nodule location (OR = 3.25; 95% CI: 1.09, 8.55), border definition (OR = 7.56; 95% CI: 1.89, 30.8), and concavity (OR = 2.58; 95% CI: 0.89, 5.64). Conclusion A set of clinically relevant radiologic features were identified that that can be easily scored in the clinical setting and may be of use to determine lung cancer risk among participants with SPNs. ^© RSNA, 2017 Online supplemental material is available for this article.

Prediction of pulmonary metastasis in pulmonary nodules (≤10 mm) detected in patients with primary extrapulmonary malignancy at thin-section staging CT

PURPOSE

To investigate the predictive clinical and imaging factors associated with pulmonary metastasis in pulmonary nodules (PNs) ≤10 mm in patients with primary extrapulmonary malignancy (PEPM) on initial CT as well as the inter-nodular imaging features in the non-solitary PNs patients, to make a more reliable diagnosis and appropriate management of the PNs at an earlier stage after detection.

MATERIALS AND METHODS

161 patients with PNs ≤10 mm were reviewed from April 2013 to December 2013. The nature of PNs were determined on the interval change in imaging features on serial CT images (158 patients) and histologically proven (three patients). Independent predictors of changed PNs on initial CT were examined by multivariate regression analysis.

RESULTS

36.6% of patients developed interval change in nodules size. The average interval of the first change was 65.0 days (29-144 days). Tumor staging of III (P = 0.011) and IV (P < 0.001), the nodules number of 2-4 (P = 0.016), 5-9 (P < 0.001) and 10-20 (P < 0.001), the nodules margin of being smooth (P = 0.001) and slight lobulated (P < 0.001), and nodules with no near short strips (P = 0.001) were significant predictors of changed PNs. For patients with non-solitary PNs, 40.2% had PNs with identical imaging features, the incidence rate of change of which (74.3%) was significantly higher compared with that of varied features (32.7%), P < 0.001; and 94.3% of patients had all nodules per patient showing consistent prognosis.

CONCLUSIONS

For PNs ≤10 mm in patients with PEPM on baseline CT, the morphological characteristics and primary malignancies stage could differentiate the majority of the PNs. The interval for further CT evaluation of uncertain PNs should be early at 1-3 months after detection, and increased alert is needed for the possibility of pulmonary metastasis when an early interval change was detected.

High-resolution Computed Tomography Features Distinguishing Benign and Malignant Lesions Manifesting as Persistent Solitary Subsolid Nodules

INTRODUCTION

We retrospectively investigated the high-resolution computed tomography features that distinguish benign lesions (BLs) from malignant lesions (MLs) appearing as persistent solitary subsolid nodules (SSNs).

MATERIALS AND METHODS

In 2015, the data from patients treated in our department with persistent solitary SSNs 5 to 30 mm in size were analyzed retrospectively. The demographic data and HRCT findings were analyzed and compared between those with BLs and MLs.

RESULTS

Of the 1934 SSNs, 94 were BLs and 1840 were MLs. One half of the MLs (920 SSNs) were randomly selected and analyzed. The BLs were classified into 2 subgroups: 28 pure ground-glass nodules (pGGNs) and 66 part-solid nodules (PSNs). After matching in each group, 56 pGGNs and 132 PSNs in the ML group were selected. In the pGGN subgroup, multivariate analysis found that a well-defined border (odds ratio [OR], 4.320; 95% confidence interval [CI], 1.534-12.168; P = .006; area under the curve, 0.705; 95% CI, 0.583-0.828; P = .002) and a higher average CT value (OR, 1.007; 95% CI, 1.001-1.013; P = .026; area under the curve, 0.715; 95% CI, 0.599-0.831; P = .001) favored the diagnosis of malignancy. In the PSN subgroup, multivariate analysis revealed that a larger size (OR, 1.084; 95% CI, 1.015-1.158; P = .016), a well-defined border (OR, 3.447; 95% CI, 1.675-7.094; P = .001), and a spiculated margin (OR, 2.735; 95% CI, 1.359-5.504; P = .005) favored the diagnosis of malignancy.

CONCLUSION

In pGGNs, a well-defined lesion border and a larger average CT value can be valuable discriminators to distinguish between MLs and BLs. In PSNs, a larger size, well-defined border, and spiculated margin had greater predictive value for malignancy.

Toward Understanding the Size Dependence of Shape Features for Predicting Spiculation in Lung Nodules for Computer-Aided Diagnosis

We analyze the importance of shape features for predicting spiculation ratings assigned by radiologists to lung nodules in computed tomography (CT) scans. Using the Lung Image Database Consortium (LIDC) data and classification models based on decision trees, we demonstrate that the importance of several shape features increases disproportionately relative to other image features with increasing size of the nodule. Our shaped-based classification results show an area under the receiver operating characteristic (ROC) curve of 0.65 when classifying spiculation for small nodules and an area of 0.91 for large nodules, resulting in a 26% difference in classification performance using shape features. An analysis of the results illustrates that this change in performance is driven by features that measure boundary complexity, which perform well for large nodules but perform relatively poorly and do no better than other features for small nodules. For large nodules, the roughness of the segmented boundary maps well to the semantic concept of spiculation. For small nodules, measuring directly the complexity of hard segmentations does not yield good results for predicting spiculation due to limits imposed by spatial resolution and the uncertainty in boundary location. Therefore, a wider range of features, including shape, texture, and intensity features, are needed to predict spiculation ratings for small nodules. A further implication is that the efficacy of shape features for a particular classifier used to create computer-aided diagnosis systems depends on the distribution of nodule sizes in the training and testing sets, which may not be consistent across different research studies.

Thin-section computed tomography-histopathologic comparisons of pulmonary focal interstitial fibrosis, atypical adenomatous hyperplasia, adenocarcinoma in situ, and minimally invasive adenocarcinoma with pure ground-glass opacity

OBJECTIVE

To retrospectively compare focal interstitial fibrosis (FIF), atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), and minimally invasive adenocarcinoma (MIA) with pure ground-glass opacity (GGO) using thin-section computed tomography (CT).

MATERIALS AND METHODS

Sixty pathologically confirmed cases were reviewed including 7 cases of FIF, 17 of AAH, 23of AIS, and 13 of MIA. All nodules kept pure ground glass appearances before surgical resection and their last time of thin-section CT imaging data before operation were collected. Differences of patient demographics and CT features were compared among these four types of lesions.

RESULTS

FIF occurred more frequently in males and smokers while the others occurred more frequently in female nonsmokers. Nodule size was significant larger in MIA (P<0.001, cut-off value=7.5mm). Nodule shape (P=0.045), margin characteristics (P<0.001), the presence of pleural indentation (P=0.032), and vascular ingress (P<0.001) were significant factors that differentiated the 4 groups. A concave margin was only demonstrated in a high proportion of FIF at 85.7% (P=0.002). There were no significant differences (all P>0.05) in age, malignant history, attenuation value, location, and presence of bubble-like lucency.

CONCLUSION

A nodule size >7.5mm increases the possibility of MIA. A concave margin could be useful for differentiation of FIF from the other malignant or pre-malignant GGO nodules. The presence of spiculation or pleural indentation may preclude the diagnosis of AAH.

Anatomy packing with hierarchical segments: an algorithm for segmentation of pulmonary nodules in CT images

BACKGROUND

This paper proposes a semantic segmentation algorithm that provides the spatial distribution patterns of pulmonary ground-glass nodules with solid portions in computed tomography (CT) images.

METHODS

The proposed segmentation algorithm, anatomy packing with hierarchical segments (APHS), performs pulmonary nodule segmentation and quantification in CT images. In particular, the APHS algorithm consists of two essential processes: hierarchical segmentation tree construction and anatomy packing. It constructs the hierarchical segmentation tree based on region attributes and local contour cues along the region boundaries. Each node of the tree corresponds to the soft boundary associated with a family of nested segmentations through different scales applied by a hierarchical segmentation operator that is used to decompose the image in a structurally coherent manner. The anatomy packing process detects and localizes individual object instances by optimizing a hierarchical conditional random field model. Ninety-two histopathologically confirmed pulmonary nodules were used to evaluate the performance of the proposed APHS algorithm. Further, a comparative study was conducted with two conventional multi-label image segmentation algorithms based on four assessment metrics: the modified Williams index, percentage statistic, overlapping ratio, and difference ratio.

RESULTS

Under the same framework, the proposed APHS algorithm was applied to two clinical applications: multi-label segmentation of nodules with a solid portion and surrounding tissues and pulmonary nodule segmentation. The results obtained indicate that the APHS-generated boundaries are comparable to manual delineations with a modified Williams index of 1.013. Further, the resulting segmentation of the APHS algorithm is also better than that achieved by two conventional multi-label image segmentation algorithms.

CONCLUSIONS

The proposed two-level hierarchical segmentation algorithm effectively labelled the pulmonary nodule and its surrounding anatomic structures in lung CT images. This suggests that the generated multi-label structures can potentially serve as the basis for developing related clinical applications.

[Management of solid pulmonary nodules]

The increasing availability of computed tomography has meant that the number of incidentally detected solitary pulmonary nodules (SPN) has greatly increased in recent years. A reasonable management of these SPN is necessary in order to firstly be able to detect malignant lesions early on and secondly to avoid upsetting the patient unnecessarily or carrying out further stressful diagnostic procedures. This review article shows how the dignity of SPNs can be estimated and based on this how the management can be accomplished taking established guidelines into consideration.

Pulmonary nodule: a comprehensive review and update

The incidental detection of solitary pulmonary nodules and ground-glass nodules has increased substantially with the use of computed tomography as a diagnostic modality and is expected to rise exponentially as lung cancer screening guidelines are more widely implemented by primary care physicians. The lesions should then be classified as low, indeterminate, or high risk for malignancy, depending on the clinical and radiological characteristics. Once classified, these lesions should be evaluated and managed as per expert consensus-based recommendations for performing follow-up computed tomography scans and tissue sampling depending on the pretest probability. When weighing the risks and benefits of further investigations, patient preference and suitability for surgery should be taken into consideration as well.

Features of resolving and nonresolving indeterminate pulmonary nodules at follow-up CT: the NELSON study

PURPOSE

To retrospectively identify features that allow prediction of the disappearance of solid, indeterminate, intraparenchymal nodules detected at baseline computed tomographic (CT) screening of individuals at high risk for lung cancer.

MATERIALS AND METHODS

The study was institutional review board approved. Participants gave informed consent. Participants with at least one noncalcified, solid, indeterminate, intraparenchymal nodule (volume range, 50-500 mm(3)) at baseline were included (964 nodules in 750 participants). According to protocol, indeterminate nodules were re-examined at a 3-month follow-up CT examination. Repeat screening was performed at years 2 and 4. A nodule was defined as resolving if it did not appear at a subsequent CT examination. Nodule resolution was regarded as spontaneous, not the effect of treatment. CT features of resolving and nonresolving (stable and malignant) nodules were compared by means of generalized estimating equations analysis.

RESULTS

At subsequent screening, 10.1% (97 of 964) of the nodules had disappeared, 77.3% (n = 75) of these at the 3-month follow-up CT and 94.8% (n = 92) at the second round of screening. Nonperipheral nodules were more likely to resolve than were peripheral nodules (odds ratio: 3.16; 95% confidence interval: 1.76, 5.70). Compared with smooth nodules, nodules with spiculated margins showed the highest probability of disappearance (odds ratio: 4.36; 95% confidence interval: 2.24, 8.49).

CONCLUSION

Approximately 10% of solid, intermediate-sized, intraparenchymal pulmonary nodules found at baseline screening for lung cancer resolved during follow-up, three-quarters of which had disappeared at the 3-month follow-up CT examination. Resolving pulmonary nodules share CT features with malignant nodules.

An update on the evaluation and management of small pulmonary nodules

The widespread utilization of chest CT scans has increased the importance of the proper evaluation of incidentally found lung nodules. The primary goal in the evaluation of these nodules is to determine whether they are malignant or benign. Clinical factors such as older age, tobacco smoking, and current or remote history of malignancy increase the pretest likelihood of malignancy. Radiological features of these nodules are important in differentiating benign from malignant lesions. However, the etiology of the lung nodules frequently remains indeterminate and requires further evaluation. The approach to the management of indeterminate lung nodules ranges between observation with repeat chest CT scan, further diagnostic studies such as PET scan or invasive procedures to obtain tissue diagnosis. This article reviews the importance of the different radiological features of lung nodules. This is followed by an update on the approach to the management of the different types of small lung nodules.

[Diagnosis and management of solitary pulmonary nodules]

目前，肺癌已跃居成为我国发病率及死亡率最高的恶性肿瘤，总体5年生存率较低；早诊早治是提高肺癌患者生存率及改善预后的关键，而早期肺癌患者常无任何症状和体征，只在影像学上表现为肺孤立性结节病变。提高对孤立性肺结节良恶性的鉴别诊断能力是临床诊治过程中的难点与热点。随着各种诊治技术的发展，孤立性肺结节病变性质的诊断准确率已大大提高。

A practical algorithmic approach to the diagnosis and management of solitary pulmonary nodules: part 2: pretest probability and algorithm

In this second part of a two-part series, we describe an algorithmic approach to the diagnosis of the solitary pulmonary nodule (SPN). An essential aspect of the evaluation of SPN is determining the pretest probability of malignancy, taking into account the significant medical history and social habits of the individual patient, as well as morphologic characteristics of the nodule. Because pretest probability plays an important role in determining the next step in the evaluation, we describe various methods the physician may use to make this determination. Subsequently, we outline a simple yet comprehensive algorithm for diagnosing a SPN, with distinct pathways for the solid and subsolid SPN.

A practical algorithmic approach to the diagnosis and management of solitary pulmonary nodules: part 1: radiologic characteristics and imaging modalities

The solitary pulmonary nodule (SPN) is frequently encountered on chest imaging and poses an important diagnostic challenge to clinicians. The differential diagnosis is broad, ranging from benign granulomata and infectious processes to malignancy. Important concepts in the evaluation of SPNs include the definition, morphologic characteristics via appropriate imaging modalities, and the calculation of pretest probability of malignancy. Morphologic differentiation of SPN into solid or subsolid types is important in the choice of follow-up and further management. In this first part of a two-part series, we describe the morphologic characteristics and various imaging modalities available to further characterize SPN. In Part 2, we will describe the determination of pretest probability of malignancy and an algorithmic approach to the diagnosis of SPN.

Imaging incidental pulmonary nodules

The incidental nodule is an increasingly common clinical conundrum. This article outlines the characteristics that allow differentiation of benign and malignant pathologies and discusses strategies for their follow up and management.

Computer-aided diagnosis systems for lung cancer: challenges and methodologies

This paper overviews one of the most important, interesting, and challenging problems in oncology, the problem of lung cancer diagnosis. Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient's chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant. This paper overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps. For each technique, various aspects of technical issues, implemented methodologies, training and testing databases, and validation methods, as well as achieved performances, are described. In addition, the paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems.

Limited value of ⁹⁹mTc depreotide single photon emission CT compared with CT for the evaluation of pulmonary lesions

OBJECTIVES

A contrast-enhanced multidetector CT (MDCT) scan is the first choice examination when evaluating patients with suspected lung cancer. However, while the clinical focus is on CT, research focus is on molecular biological methods whereby radiolabelled pharmaceuticals are injected into participants and target malignant lung tumours. We examined whether a contrast-enhanced MDCT scan supplied with an additional non-contrast enhanced high-resolution CT scan, or a newer but more expensive (99m)Tc depreotide single photon emission CT (SPECT) scan, was the better first-choice examination for the work-up of pulmonary lesions. Furthermore, we examined whether a (99m)Tc depreotide SPECT scan was an appropriate second-choice examination for patients with indeterminate lesions.

METHODS

140 participants were included in the analysis. CT images were given a malignancy potential rating of 1, 2 or 3 with higher rating being indicative of disease. (99m)Tc depreotide SPECT images were graded either positive or negative. Histopathology and CT follow-up were used as reference standard. Sensitivity, specificity and diagnostic accuracy were calculated.

RESULTS

Overall sensitivity, specificity and diagnostic accuracy of CT were 97%, 30% and 84%, respectively. Overall sensitivity, specificity and diagnostic accuracy of (99m)Tc depreotide SPECT were 94%, 58% and 76%, respectively. For indeterminate lesions sensitivity, specificity and diagnostic accuracy of (99m)Tc depreotide SPECT were 71%, 68% and 69%, respectively.

CONCLUSION

Both CT and (99m)Tc depreotide SPECT made valuable contributions to the evaluation of pulmonary lesions. (99m)Tc depreotide SPECT results were not superior to CT results and did not contribute further to the diagnostic work-up. Regarding indeterminate lesions,( 99m)Tc depreotide SPECT sensitivity was too low.

Pulmonary malignant focal ground-glass opacity nodules and solid nodules of 3cm or less: comparison of multi-detector CT features

INTRODUCTION

To evaluate the different multi-detector computed tomography (MDCT) features between pulmonary malignant focal ground-glass opacity (fGGO) nodules and solid nodules of 3cm or less in diameter.

METHODS

One hundred and five malignant solid nodules and 48 malignant fGGOs confirmed by pathology were retrospectively analysed with regard to the patient's demographic data, nodule size and MDCT features (shape, margin, interface, internal characteristics and adjacent structure). Differences were analysed using the Fisher exact test or Mann-Whitney U-test.

RESULTS

The male to female ratio of patients with malignant solid nodules (60:45) was higher than that with malignant fGGOs (18:30) (P<0.05). There was no significant difference in either patient's age (P>0.05) or nodule size (P>0.05). The frequency of irregular shape (4% vs. 21%), spiculation (57% vs. 40%), vacuole sign (11% vs. 52%) and natural air bronchograms (0% vs. 24%) was significantly different between malignant solid nodules and fGGOs. No differences were found in the frequency of lobulation, cusp angle, spine-like process, interface and adjacent structure between the two groups (P>0.05).

CONCLUSION

Malignant fGGOs and solid nodules showed mostly similar MDCT features. For malignant fGGOs, the frequency of irregular shape, vacuole sign and natural air bronchograms was higher than that in solid nodules, but the frequency of spiculation was lower than that in solid nodules.

Pulmonary nodules: a quantitative method of diagnosis by evaluating nodule perimeter difference to approximate oval using three-dimensional CT images

The purpose of this study was to investigate whether maximum nodule perimeter to the approximate oval could discriminate benign nodules from malignancy. Measurement of maximum nodule perimeter difference to the approximate oval was performed using volume-rendering images of three directions of each pulmonary nodule. The margin was then traced manually and our custom software delineated the approximate oval automatically. The maximum nodule perimeter difference was 26.5±23.3 mm for malignant and 16.6±16.9 mm for benign nodules, showing an almost statistically significant difference (P=.07). This study suggests that the maximum nodule perimeter difference to the approximate oval of the malignant nodules has a tendency to be longer than benign nodules.

High resolution spiral CT for determining the malignant potential of solitary pulmonary nodules: refining and testing the test

BACKGROUND

A solitary pulmonary nodule (SPN) may represent early stage lung cancer. Lung cancer is a devastating disease with an overall 5-year mortality rate of approximately 84% but with early detection and surgery as low as 47%. Currently a contrast-enhanced multiple-row detector CT (MDCT) scan is the first examination when evaluating patients with suspected lung cancer.

PURPOSE

To apply an additional high resolution CT (HRCT) to SPNs to test whether certain morphological characteristics are associated with malignancy, to assess the diagnostic accuracy of HRCT in the characterization of SPNs, and to address the reproducibility of all measures.

MATERIAL AND METHOD

Two hundred and thirteen participants with SPNs were included in a follow-up study. Blinded HRCT images were assessed with regard to margin risk categories (MRCs), calcification patterns and certain other characteristics and overall malignancy potential ratings (MPRs) were given. Morphological characteristics were tested against reference standard and ROC methodology was applied to assess diagnostic accuracy. Reproducibility was measured with Kappa statistics and 95% confidence intervals were computed for all results. Histopathology (90%) and CT follow-up (10%) were used as reference standard.

RESULTS

MRCs (P < 0.001), calcification patterns (P = 0.003), and pleural retraction (P < 0.001) were all statistically significantly associated to malignancy. Reproducibility was moderate to substantial. Sensitivity, specificity, and overall diagnostic accuracy of HRCT were 98%, 23% and 87%, respectively. Reproducibility was substantial.

CONCLUSION

Statistically significant associations between SPN MRCs, calcification patterns, pleural retraction and malignancy were found. HRCT yielded a very high sensitivity and a somewhat lower specificity for malignancy. Reproducibility was high.

3D Shape Analysis for Early Diagnosis of Malignant Lung Nodules

An alternative method for diagnosing malignant lung nodules by their shape rather than conventional growth rate is proposed. The 3D surfaces of the detected lung nodules are delineated by spherical harmonic analysis, which represents a 3D surface of the lung nodule supported by the unit sphere with a linear combination of special basis functions, called spherical harmonics (SHs). The proposed 3D shape analysis is carried out in five steps: (i) 3D lung nodule segmentation with a deformable 3D boundary controlled by two probabilistic visual appearance models (the learned prior and the estimated current appearance one); (ii) 3D Delaunay triangulation to construct a 3D mesh model of the segmented lung nodule surface; (iii) mapping this model to the unit sphere; (iv) computing the SHs for the surface, and (v) determining the number of the SHs to delineate the lung nodule. We describe the lung nodule shape complexity with a new shape index, the estimated number of the SHs, and use it for the K-nearest classification to distinguish malignant and benign lung nodules. Preliminary experiments on 327 lung nodules (153 malignant and 174 benign) resulted in the 93.6% correct classification (for the 95% confidence interval), showing that the proposed method is a promising supplement to current technologies for the early diagnosis of lung cancer.

3D shape analysis for early diagnosis of malignant lung nodules

An alternative method of diagnosing malignant lung nodules by their shape, rather than conventional growth rate, is proposed. The 3D surfaces of the detected lung nodules are delineated by spherical harmonic analysis that represents a 3D surface of the lung nodule supported by the unit sphere with a linear combination of special basis functions, called Spherical Harmonics (SHs). The proposed 3D shape analysis is carried out in five steps: (i) 3D lung nodule segmentation with a deformable 3D boundary controlled by a new prior visual appearance model; (ii) 3D Delaunay triangulation to construct a 3D mesh model of the segmented lung nodule surface; (iii) mapping this model to the unit sphere; (iv) computing the SHs for the surface; and (v) determining the number of the SHs to delineate the lung nodule. We describe the lung nodule shape complexity with a new shape index, the estimated number of the SHs, and use it for the K-nearest classification into malignant and benign lung nodules. Preliminary experiments on 327 lung nodules (153 malignant and 174 benign) resulted in a classification accuracy of 93.6%, showing that the proposed method is a promising supplement to current technologies for the early diagnosis of lung cancer.

Comparison of thin-section CT and pathological findings in small solid-density type pulmonary adenocarcinoma: prognostic factors from CT findings

OBJECTIVE

We divided pulmonary adenocarcinoma of ≤ 20 mm into air-containing and solid-density types based on a percentage reduction of the maximum tumor diameter in the mediastinal window image compared to the area in the lung window image on thin-section (TS) CT of ≥ 50% (air-containing type) and <50% (solid-density type). No relapse occurred in patients with air-containing type. The prognosis of solid-density type may be poor even when the tumor size is 20mm or smaller. We investigated whether CT findings for these tumors could serve as prognostic factors.

METHODS

The subjects were 105 patients with solid-density type pulmonary adenocarcinoma that was identified on TSCT and found to have a diameter of 20mm or smaller after surgical resection during the period from April 1997 to November 2004. Notches, air bronchogram, pleural retraction, spiculation, venous involvement, and ground glass opacity were examined on TSCT, and their associations with pathological findings (i.e., pleural invasion, lymphatic permeation, vascular invasion, lymph node metastasis, and Noguchi's classification) and relapse were investigated using chi-square test and Cox proportional hazards model.

RESULTS

The incidence of relapse was significantly higher in cases with notches. The incidence of notches increased with tumor growth and notches were frequent in Noguchi type D tumors, reflecting poorly differentiated adenocarcinoma. Lymphatic permeation and type D cases were independent factors associated with a poor prognosis using Cox proportional hazards model.

CONCLUSIONS

TSCT findings may be useful for prediction of the prognosis of solid-density type pulmonary adenocarcinoma.

[Morphology and edge analysis of endobronchial ultrasound images in 47 patients with pulmonary malignant lesions]

BACKGROUND AND OBJECTIVE

By analyzing morpholoy and edge of endobronchial ultrasound (EBUS) images in 47 patients with pulmonary malignant lesions. The aim of this study is to explore the role of EBUS in the diagnosis of pulmonary malignant lesions.

METHODS

From June 1, 2005 to June 30, 2006, EBUS analysis was performed in a proportion of patients with malignant or benign lesions.

RESULTS

In total 78 patients of confirmed diagnosis, 47 cases were confirmed malignant lesions, and 31 were benign lesions, male 56 cases, female 22 cases, age span from 21-80 years (58.01 +/- 13.20). The statistics of the lesion edge imaging in 78 patients showed that different position and angle did not affect the morphous of the edge; analysis of the relationship between the size of the lesion and the morphous of the edge shows that the size of the lesion does not affect the morphous of the edge; the comparison between lesions of benign and malignant shows that a clear edge is the major feature of malignant lesions, indicating certain value in diagnosis; comparison between the shape of the lesion and the property of the lesion shows no relationship between them; compared to conventional bronchoscopy operations, the EBUS operation consumes approximately 10 min, and no related complication was found.

CONCLUSION

Clear morphous of the edge in EBUS is a feature of the malignant lesion, and EBUS has certain value in the diagnosis of the malignant lesions.

[Characterization and management of incidentally detected solitary pulmonary nodules]

How to deal with solitary pulmonary nodules (SPN) which are incidentally detected by computed tomography (CT) is an increasingly important task in the era of modern multislice CT. This paper reviews the morphological and functional characteristics and their value for discrimination between benign and malignant SPNs. In particular, the importance of nodule size, growth rate, margin morphology, density, calcifications or fatty components within the nodules, the significance of cavitations or aerobronchograms, enhancement patterns at dynamic contrast-enhanced CT and findings on positron emission tomography (PET) are discussed. The Bayesian analysis to calculate the probability of malignancy is presented. Finally, flow charts demonstrate the national and international recommendations for nodule management.

Computer-aided diagnosis of lung cancer: definition and detection of ground-glass opacity type of nodules by high-resolution computed tomography

PURPOSE

The ground-glass opacity (GGO) of lung cancer is identified only subjectively on computed tomography (CT) images as no quantitative characteristic has been defined for GGOs. We sought to define GGOs quantitatively and to differentiate between GGOs and solid-type lung cancers semiautomatically with a computer-aided diagnosis (CAD).

METHODS AND MATERIALS

High-resolution CT images of 100 pulmonary nodules (all peripheral lung cancers) were collected from our clinical records. Two radiologists traced the contours of nodules and distinguished GGOs from solid areas. The CT attenuation value of each area was measured. Differentiation between cancer types was assessed by a receiver-operating characteristic (ROC) analysis.

RESULTS

The mean CT attenuation of the GGO areas was -618.4 +/- 212.2 HU, whereas that of solid areas was -68.1 +/- 230.3 HU. CAD differentiated between solidand GGO-type lung cancers with a sensitivity of 86.0% and specificity of 96.5% when the threshold value was -370 HU. Four nodules of mixed GGOs were incorrectly classified as the solid type. CAD detected 96.3% of GGO areas when the threshold between GGO and solid areas was 194 HU.

CONCLUSION

Objective definition of GGO area by CT attenuation is feasible. This method is useful for semiautomatic differentiation between GGOs and solid types of lung cancer.

Smooth or attached solid indeterminate nodules detected at baseline CT screening in the NELSON study: cancer risk during 1 year of follow-up

PURPOSE

To retrospectively determine whether baseline nodule characteristics at 3-month and 1-year volume doubling time (VDT) are predictive for lung cancer in solid indeterminate noncalcified nodules (NCNs) detected at baseline computed tomographic (CT) screening.

MATERIALS AND METHODS

The study, conducted between April 2004 and May 2006, was institutional review board approved. Patient consent was waived for this retrospective evaluation. NCNs between 5 and 10 mm in diameter (n = 891) were evaluated at 3 months and 1 year to assess growth (VDT < 400 days). Baseline assessments were related to growth at 3 months and 1 year by using chi(2) and Mann-Whitney U tests. Baseline assessments and growth were related to the presence of malignancy by using univariate and multivariate logistic regression analyses.

RESULTS

At 3 months and at 1 year, 8% and 1% of NCNs had grown, of which 15% and 50% were malignant, respectively. One-year growth was related to morphology (P < .01), margin (P < .0001), location (P < .001), and size (P < .01). All cancers were nonspherical and purely intraparenchymal, without attachment to vessels, the pleura, or fissures. In nonsmooth unattached nodules, a volume of 130 mm(3) or larger was the only predictor for malignancy (odds ratio, 6.3; 95% confidence interval [CI]: 1.7, 23.0). After the addition of information on the 3-month VDT, large volume (odds ratio, 4.9; 95% CI: 1.2, 20.1) and 3-month VDT (odds ratio, 15.6; 95% CI: 4.5, 53.5) helped predict malignancy. At 1 year, only the 1-year growth remained (odds ratio, 213.3; 95% CI: 18.7, 2430.9) as predictor for malignancy.

CONCLUSION

In smooth or attached solid indeterminate NCNs, no malignancies were found at 1-year follow-up. In nonsmooth purely intraparenchymal NCNs, size is the main baseline predictor for malignancy. When follow-up data are available, growth is a strong predictor for malignancy, especially at 1-year follow-up.

High-resolution computed tomographic findings of small peripherally located squamous cell carcinoma

With the spread of high-resolution computed tomography (HRCT) screening for lung cancer, we are increasingly faced with the need to determine whether certain small lesions are benign or malignant. The features of small adenocarcinomas have been clarified but not those of squamous cell carcinoma. The objective of our study was therefore to clarify the HRCT findings of peripherally located squamous cell carcinomas less than 2 cm in maximum dimension. Subjects consisted of 27 consecutive pathologically proven cases of peripherally located squamous cell carcinoma that were less than 2 cm in maximum dimension. HRCT findings of all 27 cases were analyzed retrospectively and independently by three radiologists who were unaware of the pathological diagnosis, and decisions were reached by consensus with special attention to 10 review points. Internal characteristic features included calcification, cavity formation, and air bronchogram. Tumor margin features included spiculation, notching, irregularity, and ground-glass opacity. Surrounding structural features consisted of pleural indentation, pulmonary emphysema, and satellite lesions. The presence of irregularity (70.4%), surrounding pulmonary emphysema (70.4%), and pleural indentation (51.9%) was observed frequently. No mass was accompanied by calcification. HRCT images of peripherally located squamous cell carcinoma suggested that the demonstration of irregularity, surrounding pulmonary emphysema, pleural indentation, and absence of calcification may contribute to the accurate CT diagnosis of small peripheral squamous cell carcinoma.