Role of baseline nodule density and changes in density and nodule features in the discrimination between benign and malignant solid indeterminate pulmonary nodules

Observational Study of the Natural Growth History of Peripheral Small-Cell Lung Cancer on CT Imaging

BACKGROUND

This study aimed to investigate the natural growth history of peripheral small-cell lung cancer (SCLC) using CT imaging.

METHODS

A retrospective study was conducted on 27 patients with peripheral SCLC who underwent at least two CT scans. Two methods were used: Method 1 involved direct measurement of nodule dimensions using a calliper, while Method 2 involved tumour lesion segmentation and voxel volume calculation using the "py-radiomics" package in Python. Agreement between the two methods was assessed using the intraclass correlation coefficient (ICC). Volume doubling time (VDT) and growth rate (GR) were used as evaluation indices for SCLC growth, and growth distribution based on GR and volume measurements were depicted. We collected potential factors related to imaging VDT and performed a differential analysis. Patients were classified into slow-growing and fast-growing groups based on a VDT cut-off point of 60 days, and univariate analysis was used to identify factors influencing VDT.

RESULTS

Median VDT calculated by the two methods were 61 days and 71 days, respectively, with strong agreement. All patients had continuously growing tumours, and none had tumours that decreased in size or remained unchanged. Eight patients showed possible growth patterns, with six possibly exhibiting exponential growth and two possibly showing Gompertzian growth. Tumours deeper in the lung grew faster than those adjacent to the pleura.

CONCLUSIONS

Peripheral SCLC tumours grow rapidly and continuously without periods of nongrowth or regression. Tumours located deeper in the lung tend to grow faster, but further research is needed to confirm this finding.

Radiomic Analysis of Pulmonary Nodules for Distinguishing Malignancy From Benignancy: The Value of Using Iodine Maps From Dual-Energy Computed Tomography

OBJECTIVE

The aim of the study is to investigate the diagnostic accuracy of radiomics on iodine maps from dual-energy computed tomography (DECT) in distinguishing lung cancer from benign pulmonary nodules.

METHODS

This retrospective study was approved by the institutional review board, and written informed consent was waived. A total of 109 patients with 55 malignant nodules and 62 benign nodules underwent contrast-enhanced DECT. Eight iodine uptake parameters on iodine maps generated by DECT were calculated and established a predictive model. Eighty-seven radiomics features of entire tumor were extracted from iodine maps and established a radiomics model. The iodine uptake model and radiomics model were independently built based on the highly reproducible features using the least absolute shrinkage and selection operator method. The diagnostic accuracy of 2 models were assessed using receiver operating curve analysis. For external validation, 47 patients (25 benign and 22 malignant) from another hospital were assigned to testing data set.

RESULTS

All iodine uptake features showed significant association with malignancy (P < 0.01) and 2 selected features (mean value of virtual noncontrast images and mean value of vital part on contrast-enhanced image) constituted the iodine model. The radiomics model comprised 2 features (original shape sphericity and original glszm small area high gray level emphasis), which showed good discrimination both in the training cohort (area under the curve, 0.957) and validation cohort (area under the curve, 0.800). Radiomics model showed superior performance than iodine uptake model (accuracy, 89.7% vs 80.6%).

CONCLUSIONS

Radiomics model extracted from iodine maps provided a robust diagnostic tool for discriminating pulmonary malignant nodules and had high potential in clinical application.

Explainable Machine Learning Solution for Observing Optimal Surgery Timings in Thoracic Cancer Diagnosis

In this paper, we introduce an AI-based procedure to estimate and assist in choosing the optimal surgery timing, in the case of a thoracic cancer diagnostic, based on an explainable machine learning model trained on a knowledge base. This decision is usually taken by the surgeon after examining a set of clinical parameters and their evolution in time. Therefore, it is sometimes subjective, it depends heavily on the previous experience of the surgeon, and it might not be confirmed by the histopathological exam. Therefore, we propose a pipeline of automatic processing steps with the purpose of inferring the prospective result of the histopathologic exam, generating an explanation of why this inference holds, and finally, evaluating it against the conclusive opinion of an experienced surgeon. To obtain an accurate practical result, the training dataset is labeled manually by the thoracic surgeon, creating a training knowledge base that is not biased towards clinical practice. The resulting intelligent system benefits from both the precision of a classical expert system and the flexibility of deep neural networks, and it is supposed to avoid, at maximum, any possible human misinterpretations and provide a factual estimate for the proper timing for surgical intervention. Overall, the experiments showed a 7% improvement on the test set compared with the medical opinion alone. To enable the reproducibility of the AI system, complete handling of a case study is presented from both the medical and technical aspects.

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.

[Expert Consensus for Thermal Ablation of Pulmonary Subsolid Nodules (2021 Edition)]

局部热消融技术在肺部结节治疗领域正处在起步与发展阶段，为了肺结节热消融治疗的临床实践和规范发展，由“中国医师协会肿瘤消融治疗技术专家组”“中国医师协会介入医师分会肿瘤消融专业委员会”“中国抗癌协会肿瘤消融治疗专业委员会”“中国临床肿瘤学会消融专家委员会”组织多学科国内有关专家，讨论制定了“热消融治疗肺部亚实性结节专家共识（2021年版）”。主要内容包括：①肺部亚实性结节的临床评估；②热消融治疗肺部亚实性结节技术操作规程、适应证、禁忌证、疗效评价和相关并发症；③存在的问题和未来发展方向。

Clinical features and outcomes of patients with stage I multiple primary lung cancers

The number of patients with multiple primary lung cancers (MPLC) is rising. We studied the clinical features and factors related to outcomes of MPLC patients using the database of surgically resected lung cancer (LC) cases compiled by the Japanese Joint Committee of Lung Cancer Registry. From the 18 978 registered cases, 9689 patients with clinical stage I non‐small‐cell lung cancer who achieved complete resection were extracted. Tumors were defined as synchronous MPLC when multiple LC was simultaneously resected or treatment was carried out within 2 years after the initial surgery; metachronous MPLC was defined as second LC treated more than 2 years after the initial surgery. Of these cases, 579 (6.0%) were synchronous MPLC and 477 (5.0%) metachronous MPLC, with 51 overlapping cases. Female sex, nonsmoker, low consolidation‐tumor ratio (CTR), and adenocarcinoma were significantly more frequent in the synchronous MPLC group, whereas patients with metachronous MPLC had higher frequencies of male sex, smoker, chronic obstructive pulmonary disease (COPD), and nonadenocarcinoma. There was no significant difference in survival rate between patients with and without synchronous or metachronous MPLC. Age, gender, CTR for second LC, and histological combination of primary and second LC were prognostic indicators for both types of MPLC. Logistic regression analysis showed that female sex, history of malignant disease other than LC, and COPD were risk factors for MPLC incidence. The present findings could have major implications regarding MPLC diagnosis and identification of independent prognostic factors, and provide valuable information for postoperative management of patients with MPLC.

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

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

Small Cell Lung Cancer Therapeutic Responses Through Fractal Measurements: From Radiology to Mitochondrial Biology

Small cell lung cancer (SCLC) is an aggressive neuroendocrine disease with an overall 5 year survival rate of ~7%. Although patients tend to respond initially to therapy, therapy-resistant disease inevitably emerges. Unfortunately, there are no validated biomarkers for early-stage SCLC to aid in early detection. Here, we used readouts of lesion image characteristics and cancer morphology that were based on fractal geometry, namely fractal dimension (FD) and lacunarity (LC), as novel biomarkers for SCLC. Scanned tumors of patients before treatment had a high FD and a low LC compared to post treatment, and this effect was reversed after treatment, suggesting that these measurements reflect the initial conditions of the tumor, its growth rate, and the condition of the lung. Fractal analysis of mitochondrial morphology showed that cisplatin-treated cells showed a discernibly decreased LC and an increased FD, as compared with control. However, treatment with mdivi-1, the small molecule that attenuates mitochondrial division, was associated with an increase in FD as compared with control. These data correlated well with the altered metabolic functions of the mitochondria in the diseased state, suggesting that morphological changes in the mitochondria predicate the tumor’s future ability for mitogenesis and motogenesis, which was also observed on the CT scan images. Taken together, FD and LC present ideal tools to differentiate normal tissue from malignant SCLC tissue as a potential diagnostic biomarker for SCLC.

Quantitative assessment of nonsolid pulmonary nodule volume with computed tomography in a phantom study

Background

To assess the volumetric measurement of small (≤1 cm) nonsolid nodules with computed tomography (CT), focusing on the interaction of state of the art iterative reconstruction (IR) methods and dose with nodule densities, sizes, and shapes.

Methods

Twelve synthetic nodules [5 and 10 mm in diameter, densities of -800, -630 and -10 Hounsfield units (HU), spherical and spiculated shapes] were scanned within an anthropomorphic phantom. Dose [computed tomography scan dose index (CTDI_vol)] ranged from standard (4.1 mGy) to below screening levels (0.3 mGy). Data was reconstructed using filtered back-projection and two state-of-the-art IR methods (adaptive and model-based). Measurements were extracted with a previously validated matched filter-based estimator. Analysis of accuracy and precision was based on evaluation of percent bias (PB) and the repeatability coefficient (RC) respectively.

Results

Density had the most important effect on measurement error followed by the interaction of density with nodule size. The nonsolid -630 HU nodules had high accuracy and precision at levels comparable to solid (-10 HU) nonsolid, regardless of reconstruction method and with CTDI_vol as low as 0.6 mGy. PB was <5% and <11% for the 10- and 5-mm in nominal diameter -630 HU nodules respectively, and RC was <5% and <12% for the same nodules. For nonsolid -800 HU nodules, PB increased to <11% and <30% for the 10- and 5-mm nodules respectively, whereas RC increased slightly overall but varied widely across dose and reconstruction algorithms for the 5-mm nodules. Model-based IR improved measurement accuracy for the 5-mm, low-density (-800, -630 HU) nodules. For other nodules the effect of reconstruction method was small. Dose did not affect volumetric accuracy and only affected slightly the precision of 5-mm nonsolid nodules.

Conclusions

Reasonable values of both accuracy and precision were achieved for volumetric measurements of all 10-mm nonsolid nodules, and for the 5-mm nodules with -630 HU or higher density, when derived from scans acquired with below screening dose levels as low as 0.6 mGy and regardless of reconstruction algorithm.

Predictors of primary lung cancer in a solitary pulmonary lesion after a previous malignancy

OBJECTIVE

A solitary pulmonary lesion in patients with a history of malignancy may be either primary lung cancer or a metastatic lung tumor or benign nodule. We retrospectively examined the preoperative predictive factors for determining the type of pathology.

METHODS

We used an exact logistic regression analysis to identify radiological and clinical predictors of primary lung cancer. The study included 187 patients who underwent pulmonary resection for a solitary pulmonary lesion and had received previous treatment for a malignancy.

RESULTS

There were 107 patients with primary lung cancer, 74 with metastatic lung tumors, and 6 with benign lesions. The previous malignancy included colorectal cancer in 71 patients. A disease-free interval exceeding 5 years and ground-glass opacity were found in 27.0% (20/74) and 1.4% (1/74) of metastatic lung tumors, respectively. Multivariate logistic regression analysis demonstrated that spiculation [adjusted odds ratio (a-OR), 1.74; 95% confidence interval (CI), 1.09-2.86], pleural indentation (a-OR 1.99, 95% CI 1.24-3.29), and ground-glass opacity (a-OR 5.28, 95% CI 2.61-13.1) on high-resolution computed tomography, maximum standardized uptake value (a-OR 1.14, 95% CI 1.02-1.29), current and former smokers (a-OR 1.96, 95% CI 1.21-3.30), and previous malignancy other than colorectal cancer (a-OR 2.02, 95% CI 1.26-3.37) were associated with primary lung cancer.

CONCLUSIONS

A combination of radiological findings, smoking history, and type of previous malignancy can improve the ability to predict primary lung cancer in the presence of a solitary pulmonary lesion that appears after previous treatment for a malignancy.

Lung-RADS Category 4X: Does It Improve Prediction of Malignancy in Subsolid Nodules?

Purpose To evaluate the added value of Lung CT Screening Reporting and Data System (Lung-RADS) assessment category 4X over categories 3, 4A, and 4B for differentiating between benign and malignant subsolid nodules (SSNs). Materials and Methods SSNs on all baseline computed tomographic (CT) scans from the National Lung Cancer Trial that would have been classified as Lung-RADS category 3 or higher were identified, resulting in 374 SSNs for analysis. An experienced screening radiologist volumetrically segmented all solid cores and located all malignant SSNs visible on baseline scans. Six experienced chest radiologists independently determined which nodules to upgrade to category 4X, a recently introduced category for lesions that demonstrate additional features or imaging findings that increase the suspicion of malignancy. Malignancy rates of purely size-based categories and category 4X were compared. Furthermore, the false-positive rates of category 4X lesions were calculated and observer variability was assessed by using Fleiss κ statistics. Results The observers upgraded 15%-24% of the SSNs to category 4X. The malignancy rate for 4X nodules varied from 46% to 57% per observer and was substantially higher than the malignancy rates of categories 3, 4A, and 4B SSNs without observer intervention (9%, 19%, and 23%, respectively). On average, the false-positive rate for category 4X nodules was 7% for category 3 SSNs, 7% for category 4A SSNs, and 19% for category 4B SSNs. Of the falsely upgraded benign lesions, on average 27% were transient. The agreement among the observers was moderate, with an average κ value of 0.535 (95% confidence interval: 0.509, 0.561). Conclusion The inclusion of a 4X assessment category for lesions suspicious for malignancy in a nodule management tool is of added value and results in high malignancy rates in the hands of experienced radiologists. Proof of the transient character of category 4X lesions at short-term follow-up could avoid unnecessary invasive management. ^© RSNA, 2017.

Volume versus diameter assessment of small pulmonary nodules in CT lung cancer screening

Currently, lung cancer screening by low-dose chest CT is implemented in the United States for high-risk persons. A disadvantage of lung cancer screening is the large number of small-to-intermediate sized lung nodules, detected in around 50% of all participants, the large majority being benign. Accurate estimation of nodule size and growth is essential in the classification of lung nodules. Currently, manual diameter measurements are the standard for lung cancer screening programs and routine clinical care. However, European screening studies using semi-automated volume measurements have shown higher accuracy and reproducibility compared to diameter measurements. In addition to this, with the optimization of CT scan techniques and reconstruction parameters, as well as advances in segmentation software, the accuracy of nodule volume measurement can be improved even further. The positive results of previous studies on volume and diameter measurements of lung nodules suggest that manual measurements of nodule diameter may be replaced by semi-automated volume measurements in the (near) future.

A mathematical simulation to assess variability in lung nodule size measurement associated with nodule-slice position

The purpose of this study is to assess the variance and error in nodule diameter measurement associated with variations in nodule-slice position in cross-sectional imaging. A computer program utilizing a standard geometric model was used to simulate theoretical slices through a perfectly spherical nodule of known size, position, and density within a background of "lung" of known fixed density. Assuming a threshold density, partial volume effect of a voxel was simulated using published slice and pixel sensitivity profiles. At a given slice thickness and nodule size, 100 scans were simulated differing only in scan start position, then repeated for multiple node sizes at three simulated slice thicknesses. Diameter was measured using a standard, automated algorithm. The frequency of measured diameters was tabulated; average errors and standard deviations (SD) were calculated. For a representative 5-mm nodule, average measurement error ranged from +10 to -23% and SD ranged from 0.07 to 0.99 mm at slice thicknesses of 0.75 to 5 mm, respectively. At fixed slice thickness, average error and SD decreased from peak values as nodule size increased. At fixed nodule size, SD increased as slice thickness increased. Average error exhibited dependence on both slice thickness and threshold. Variance and error in nodule diameter measurement associated with nodule-slice position exists due to geometrical limitations. This can lead to false interpretations of nodule growth or stability that could affect clinical management. The variance is most pronounced at higher slice thicknesses and for small nodule sizes. Measurement error is slice thickness and threshold dependent.

Screening for lung cancer with low-dose computed tomography: a review of current status

Screening using low-dose computed tomography (CT) represents an exciting new development in the struggle to improve outcomes for people with lung cancer. Randomised controlled evidence demonstrating a 20% relative lung cancer mortality benefit has led to endorsement of screening by several expert bodies in the US and funding by healthcare providers. Despite this pivotal result, many questions remain regarding technical and logistical aspects of screening, cost-effectiveness and generalizability to other settings. This review discusses the rationale behind screening, the results of on-going trials, potential harms of screening and current knowledge gaps.

Computed tomography quantitative analysis of components: a new method monitoring the growth of pulmonary nodule

BACKGROUND

The estimation of the growth of solitary pulmonary nodules by using non-invasive methods is increasingly gaining clinical importance for performing the timely adequate treatment of these nodules.

PURPOSE

To evaluate the application value of computed tomography (CT) quantitative analysis of components for dynamic assessment of the growth of solitary pulmonary nodules, and compare this approach with three-dimensional (3D) volumetric measurement of pulmonary nodules.

MATERIAL AND METHODS

The imaging data of 21 patients who had undergone multiple follow-up CT scans for solitary pulmonary nodules were retrospectively analyzed, and the total volume of pulmonary nodules and the percentage change in the total volume of pulmonary nodules after multiple follow-up CT scans were measured using 3D volume measurement software. The volume of solid components in pulmonary nodules was measured using CT quantitative analysis; the percentage change in the volume of solid components across examinations was calculated; and the percentage change in the total volume of pulmonary nodules was compared and contrasted with the percentage change in the volume of solid components in the pulmonary nodules.

RESULTS

All 21 cases were malignant adenocarcinomas. In the 21 cases of malignant nodules, the 3D volumes of the nodules and solid components were both increased, with the percentage change in the volume of the solid components (115.78-418.91%, 130.45 ± 119.48) significantly different from the percentage change in the total volume of the nodules (78.56-105.73% , 42.34 ± 32.17) (P = 0.02).

CONCLUSION

By measuring volume changes in solid components in the nodules, CT quantitative analysis offers more sensitive and earlier evaluation of the dynamic growth of the nodules than measurement of volume changes in the nodules alone.

CT volumetry of artificial pulmonary nodules using an ex vivo lung phantom: influence of exposure parameters and iterative reconstruction on reproducibility

OBJECTIVES

To evaluate the influence of exposure parameters and raw-data based iterative reconstruction (IR) on the measurement variability of computer-aided nodule volumetry on chest multidetector computed tomography (MDCT).

MATERIALS AND METHODS

N=7 porcine lung explants were inflated in a dedicated ex vivo phantom and prepared with n=162 artificial nodules. MDCT was performed eight consecutive times (combinations of 120 and 80 kV with 120, 60, 30 and 12 mAs), and reconstructed with filtered back projection (FBP) and IR. Nodule volume and diameter were measured semi-automatically with dedicated software. The absolute percentage measurement error (APE) was computed in relation to the 120 kV 120 mAs acquisition. Noise was recorded for each nodule in every dataset.

RESULTS

Mean nodule volume and diameter were 0.32 ± 0.15 ml and 12.0 ± 2.6mm, respectively. Although IR reduced noise by 24.9% on average compared to FBP (p<0.007), APE with IR was equal to or slightly higher than with FBP. Mean APE for volume increased significantly below a volume computed tomography dose index (CTDI) of 1.0 mGy: for 120 kV 12 mAs APE was 3.8 ± 6.2% (FBP) vs. 4.0 ± 5.2% (IR) (p<0.007); for 80 kV 12 mAs APE was 8.0 ± 13.0% vs. 9.3 ± 15.8% (n.s.), respectively. Correlating APE with image noise revealed that at identical noise APE was higher with IR than with FBP (p<0.05).

CONCLUSIONS

Computer-aided volumetry is robust in a wide range of exposure settings, and reproducibility is reduced at a CTDI below 1.0 mGy only, but the error rate remains clinically irrelevant. Noise reduction by IR is not detrimental for measurement error in the setting of semi-automatic nodule volumetry on chest MDCT.

Solitary pulmonary nodule: A diagnostic algorithm in the light of current imaging technique

The solitary pulmonary nodule (SPN) is frequently seen on chest radiographs and computed tomography (CT). The finding of a SPN usually provokes a flurry of clinical and imaging activity as an SPN in at-risk population is an alert signal of possible lung cancer. The frequency of malignant nodules in a given population is variable and depends on the endemicity of granulomatous disease. The percentage of malignant nodules also rises when dealing with at-risk population. The problem is compounded by the fact that with the present generation of CT scanners, 1-2 mm nodules are discovered in approximately half of the smokers aged 50 years or older scanned. A variety of management approaches are applied in the work-up of SPN often requiring evaluation over a long period of time to establish a benign or malignant diagnosis. Comparison with previous imaging studies and morphologic evaluation of the size, margins, and internal characteristics are usually the first step in the evaluation of these nodules. It is often necessary to use additional imaging techniques and occasionally invasive procedures such a percutaneous needle lung or a surgical biopsy. Until recently, the guidelines for follow-up of indeterminate noncalcified nodules detected on nonscreening CT was a minimum of 2 years. However, during the past few years due to further refinements in CT technology and better understanding of tumor behavior, it has prompted a revision of the guidelines of the follow-up of small indeterminate nodules. These guidelines have been endorsed by the Fleischner Society.

Image analysis of small pulmonary nodules identified by computed tomography

Detection of small pulmonary nodules has markedly increased as computed tomography (CT) technology has advanced and interpretation evolved from viewing small CT images on film to magnified images on large, high-resolution computer monitors. Despite these advances, determining the etiology of a lung nodule short of major surgery remains problematic. Initial nodule size is a major criterion in evaluating the risk for malignancy, and the majority of CT detected nodules are <10 mm in diameter. Also, the likelihood that the nodule is a lung cancer increases with increasing age and smoking history, and such clinical information needs to be integrated into algorithms that guide the workup of such nodules. Baseline and annual repeat screening results are also very helpful in developing and assessing the usefulness of such algorithms. Based on CT morphology, subtypes of nodules have been identified; today nodules are routinely classified as being solid, part-solid, or nonsolid. It has been shown that part-solid nodules have a higher frequency of being malignant than solid or nonsolid ones. Other nodule characteristics such as spiculation are useful, although granulomas and fibrosis also have such features, so these characteristics have not been as useful as nodule-growth assessment. Depending on the aggressiveness of the lung cancer and the size of the nodule when it is initially seen, a follow-up CT scan 1-3 months after the first CT scan can identify those nodules with growth at a malignant rate. Software has been developed by all CT scanner manufacturers for such growth assessment, but the inherent variability of such assessments needs further development. Nodule-growth assessment based on 2-dimensional approaches is limited; therefore, software has been developed for the 3-dimensional assessment of growth. Different approaches for such growth assessment have been developed, either using automated computer segmentation techniques or hybrid methods that allow the radiologist to adjust such segmentation. There are, however, inherent reasons for variability in such measurements that need to be carefully considered, and this, together with continued technologic advances and integration of the relevant clinical information, will allow for individualization of the algorithms for the workup of small pulmonary nodules.

Early lung cancer detection using the self-evaluation scoring questionnaire and chest digital radiography: a 3-year follow-up study in China

The standard definition of high-risk individuals for lung cancer was not uniform and the value of chest digital radiography (DR) in lung cancer screening was still unproven. The aim of this study was to assess whether the original questionnaire named as "Self-evaluation Scoring Questionnaire for High-risk Individuals of Lung Cancer" combined with DR examinations could detect early stage of lung cancer effectively. The Self-evaluation Scoring Questionnaire for High-risk Individuals of Lung Cancer had been designed in previous studies. Subjects with scores over 116 points were regarded as high-risk individuals and underwent the current DR scans at least once a year from 2007 to 2009. Noncalcified nodules with a diameter over 30 mm, along with enlarged pulmonary hilus and atelectasis, were considered to be positive and subjected to further special examinations. Efficacy of the scoring questionnaire combined with DR scans was estimated by 3-year results. Among 1,537 subjects, 13, 11, and 7 were diagnosed with lung cancer in the first, second, and third year, respectively, indicating the detection rate of 2.02 % (31/1,537). In addition, 77.42 % (24/31) of the patients were in stage I and 51.61 % (16/31) were adenocarcinomas. For the 31 cases, 28 were defined as detected cancers, while the other three were interval ones, only accounting for 0.20 % (3/1,504) of individuals with negative judgments. The protocol of Self-evaluation Scoring Questionnaire for High-risk Individuals of Lung Cancer combined with DR scans is a cost-effective and safe approach to detect early stage of lung cancer.

NELSON lung cancer screening study

The Dutch-Belgian Randomized Lung Cancer Screening Trial (Dutch acronym: NELSON study) was designed to investigate whether screening for lung cancer by low-dose multidetector computed tomography (CT) in high-risk subjects will lead to a decrease in 10-year lung cancer mortality of at least 25% compared with a control group without screening. Since the start of the NELSON study in 2003, 7557 participants underwent CT screening, with scan rounds in years 1, 2, 4 and 6. In the current review, the design of the NELSON study including participant selection and the lung nodule management protocol, as well as results on validation of CT screening and first results on lung cancer screening are described.

The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans

PURPOSE

The development of computer-aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography (CT) scans. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process.

METHODS

Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories ("nodule > or =3 mm," "nodule <3 mm," and "non-nodule > or =3 mm"). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus.

RESULTS

The Database contains 7371 lesions marked "nodule" by at least one radiologist. 2669 of these lesions were marked "nodule > or =3 mm" by at least one radiologist, of which 928 (34.7%) received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings.

CONCLUSIONS

The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.

Pretreatment assessment for the optimal management of early-stage lung cancer

Optimal management of non-small cell lung cancer requires treatment approach to be tailored to both the particular disease stage and the overall health and functional status of the patient. Even though surgical resection by means of an anatomic lobectomy remains the treatment of choice with the goal of cure for early-stage lung cancer, it is an invasive procedure with associated morbidity and mortality. Although these risks continue to decrease in the modern era with improvements in surgical technique and perioperative management, the risks are elevated in patients with associated medical comorbidities. As a consequence, patients at potentially increased or high risk for surgical lobectomy need to be identified by a structured preoperative assessment. This has gained increasing importance, given the emergence of alternative treatment approaches such as minimally invasive surgery, less extensive pulmonary resection, and stereotactic body radiation therapy. We review the clinical approach to suspected early-stage lung cancer based on a tumor and patient-centered stratification of risk and benefit.

Multilevel binomial logistic prediction model for malignant pulmonary nodules based on texture features of CT image

PURPOSE

To introduce multilevel binomial logistic prediction model-based computer-aided diagnostic (CAD) method of small solitary pulmonary nodules (SPNs) diagnosis by combining patient and image characteristics by textural features of CT image.

MATERIALS AND METHODS

Describe fourteen gray level co-occurrence matrix textural features obtained from 2171 benign and malignant small solitary pulmonary nodules, which belongs to 185 patients. Multilevel binomial logistic model is applied to gain these initial insights.

RESULTS

Five texture features, including Inertia, Entropy, Correlation, Difference-mean, Sum-Entropy, and age of patients own aggregating character on patient-level, which are statistically different (P<0.05) between benign and malignant small solitary pulmonary nodules.

CONCLUSION

Some gray level co-occurrence matrix textural features are efficiently descriptive features of CT image of small solitary pulmonary nodules, which can profit diagnosis of earlier period lung cancer if combined patient-level characteristics to some extent.