Detection failures in spiral CT screening for lung cancer: analysis of CT findings

Incidental pulmonary nodules may lead to a high proportion of early-stage lung cancer: but it requires more than a high CT volume to achieve this

Background

The management of pulmonary nodules plays a critical role in early detection of lung cancer. Computed tomography (CT) has led to a stage-shift towards early-stage lung cancer, but regional differences in survival rates have been reported in Denmark. This study aimed to evaluate whether variations in nodule management among Danish health regions contributed to these differences.

Material and Methods

The Danish Health Data Authority and Danish Lung Cancer Registry provided data on CT usage and lung cancer stage distribution, respectively. Auditing of lung cancer stage IA patient referrals and nodule management of stage IV lung cancer patients was conducted in seven Danish lung cancer investigation centers, covering four of the five Danish health regions. CT scans were performed up to 2 years before the patients' diagnosis from 2019 to 2021.

Results

CT usage has increased steadily in Denmark over the past decade, with a simultaneous increase in the proportion of early-stage lung cancers, particularly stage IA. However, one Danish health region, Region Zealand, exhibited lower rates of early-stage lung cancer and overall survival despite a CT usage roughly similar to that of the other health regions. The audit did not find significant differences in pulmonary nodule management or a higher number of missed nodules by radiologists in this region compared to others.

Conclusion

This study suggests that a high CT scan volume alone is not sufficient for the early detection of lung cancer. Factors beyond hospital management practices, such as patient-related delays in socioeconomically disadvantaged areas, may contribute to regional differences in survival rates. This has implications for future strategies for reducing these differences.

Solitary Pulmonary Nodule Evaluation: Pearls and Pitfalls

Lung nodules are frequently encountered while interpreting chest CTs and are challenging to detect, characterize, and manage given they can represent both benign or malignant etiologies. An understanding of features associated with malignancy and causes of interpretive pitfalls is helpful to avoid misdiagnoses. This review addresses pertinent topics related to the etiologies for missed lung nodules on radiography and CT. Additionally, CT imaging technical pitfalls and challenges in addition to issues in the evaluation of nodule morphology, attenuation, and size will be discussed. Nodule management guidelines will be addressed as well as recent investigations that further our understanding of lung nodules.

Validation of a deep learning computer aided system for CT based lung nodule detection, classification, and growth rate estimation in a routine clinical population

OBJECTIVE

In this study, we evaluated a commercially available computer assisted diagnosis system (CAD). The deep learning algorithm of the CAD was trained with a lung cancer screening cohort and developed for detection, classification, quantification, and growth of actionable pulmonary nodules on chest CT scans. Here, we evaluated the CAD in a retrospective cohort of a routine clinical population.

MATERIALS AND METHODS

In total, a number of 337 scans of 314 different subjects with reported nodules of 3-30 mm in size were included into the evaluation. Two independent thoracic radiologists alternately reviewed scans with or without CAD assistance to detect, classify, segment, and register pulmonary nodules. A third, more experienced, radiologist served as an adjudicator. In addition, the cohort was analyzed by the CAD alone. The study cohort was divided into five different groups: 1) 178 CT studies without reported pulmonary nodules, 2) 95 studies with 1-10 pulmonary nodules, 23 studies from the same patients with 3) baseline and 4) follow-up studies, and 5) 18 CT studies with subsolid nodules. A reference standard for nodules was based on majority consensus with the third thoracic radiologist as required. Sensitivity, false positive (FP) rate and Dice inter-reader coefficient were calculated.

RESULTS

After analysis of 470 pulmonary nodules, the sensitivity readings for radiologists without CAD and radiologist with CAD, were 71.9% (95% CI: 66.0%, 77.0%) and 80.3% (95% CI: 75.2%, 85.0%) (p < 0.01), with average FP rate of 0.11 and 0.16 per CT scan, respectively. Accuracy and kappa of CAD for classifying solid vs sub-solid nodules was 94.2% and 0.77, respectively. Average inter-reader Dice coefficient for nodule segmentation was 0.83 (95% CI: 0.39, 0.96) and 0.86 (95% CI: 0.51, 0.95) for CAD versus readers. Mean growth percentage discrepancy of readers and CAD alone was 1.30 (95% CI: 1.02, 2.21) and 1.35 (95% CI: 1.01, 4.99), respectively.

CONCLUSION

The applied CAD significantly increased radiologist's detection of actionable nodules yet also minimally increasing the false positive rate. The CAD can automatically classify and quantify nodules and calculate nodule growth rate in a cohort of a routine clinical population. Results suggest this Deep Learning software has the potential to assist chest radiologists in the tasks of pulmonary nodule detection and management within their routine clinical practice.

Significance of intra-nodular vessel sign in differentiating benign and malignant pulmonary ground-glass nodules

BACKGROUND

The presence of pulmonary vessels inside ground-glass nodules (GGNs) of different nature is a very common occurrence. This study aimed to reveal the significance of pulmonary vessels displayed in GGNs in their diagnosis and differential diagnosis.

RESULTS

A total of 149 malignant and 130 benign GGNs confirmed by postoperative pathological examination were retrospectively enrolled in this study. There were significant differences in size, shape, nodule-lung interface, pleural traction, lobulation, and spiculation (each p < 0.05) between benign and malignant GGNs. Compared with benign GGNs, intra-nodular vessels were more common in malignant GGNs (67.79% vs. 54.62%, p = 0.024), while the vascular categories were similar (p = 0.663). After adjusting the nodule size and the distance between the nodule center and adjacent pleura [radius-distance ratio, RDR], the occurrences of internal vessels between them were similar. The number of intra-nodular vessels was positively correlated with nodular diameter and RDR. Vascular changes were more common in malignant than benign GGNs (52.48% vs. 18.31%, p < 0.0001), which mainly manifested as distortion and/or dilation of pulmonary veins (61.19%). The occurrence rate, number, and changes of internal vessels had no significant differences among all the pre-invasive and invasive lesions (each p > 0.05).

CONCLUSIONS

The incidence of internal vessels in GGNs is mainly related to their size and the distance between nodule and pleura rather than the pathological nature. However, GGNs with dilated or distorted internal vessels, especially pulmonary veins, have a higher possibility of malignancy.

Clinical concordance with Image Gently guidelines for pediatric computed tomography: a study across 663,417 CT scans at 53 clinical facilities

BACKGROUND

Managing patient radiation dose in pediatric computed tomography (CT) examinations is essential. Some organizations, most notably Image Gently, have suggested techniques to lower dose to pediatric patients and mitigate risk while maintaining image quality.

OBJECTIVE

We sought to validate whether institutions are observing Image Gently guidelines in practice.

MATERIALS AND METHODS

Dose-relevant data from 663,417 abdomen-pelvis and chest CT scans were obtained from 53 facilities. Patients were assigned arbitrary age cohorts with a minimum size of n=12 patients in each age group, for statistical purposes. All pediatric (<19 years old) cohorts at a given facility were compared to the adult cohort by a Kruskal-Wallis test for each of the four scan parameters - (1) x-ray tube kilovoltage (kV), (2) tube-current-by-exposure-time product (tube mAs), (3) scan pitch and (4) tube rotation time - to assess whether the distribution of values in the pediatric cohorts differed from the adult cohort. The same was repeated with volume CT dose index (CTDI_vol) and size-specific dose estimate (SSDE) to assess whether pediatric cohorts received less dose than adult cohorts. A P-value of <0.05 was deemed significant.

RESULTS

Across the 150 pediatric cohorts, 134 had scan parameters that were more child-sized than their adult counterparts. In 128 of these 134 pediatric cohorts, the CTDI_vol was less than the adult counterpart. In 111 of these 128 pediatric cohorts, the SSDE was less than the adult counterpart.

CONCLUSION

The study reaffirms that in practice, Image Gently's suggestions of lowering tube mAs and peak kilovoltage are commonly employed and effective at reducing pediatric CT dose.

Missed lung cancer: when, where, and why?

Missed lung cancer is a source of concern among radiologists and an important medicolegal challenge. In 90% of the cases, errors in diagnosis of lung cancer occur on chest radiographs. It may be challenging for radiologists to distinguish a lung lesion from bones, pulmonary vessels, mediastinal structures, and other complex anatomical structures on chest radiographs. Nevertheless, lung cancer can also be overlooked on computed tomography (CT) scans, regardless of the context, either if a clinical or radiologic suspect exists or for other reasons. Awareness of the possible causes of overlooking a pulmonary lesion can give radiologists a chance to reduce the occurrence of this eventuality. Various factors contribute to a misdiagnosis of lung cancer on chest radiographs and on CT, often very similar in nature to each other. Observer error is the most significant one and comprises scanning error, recognition error, decision-making error, and satisfaction of search. Tumor characteristics such as lesion size, conspicuity, and location are also crucial in this context. Even technical aspects can contribute to the probability of skipping lung cancer, including image quality and patient positioning and movement. Albeit it is hard to remove missed lung cancer completely, strategies to reduce observer error and methods to improve technique and automated detection may be valuable in reducing its likelihood.

Multiscale Rotation-Invariant Convolutional Neural Networks for Lung Texture Classification

We propose a new multiscale rotation-invariant convolutional neural network (MRCNN) model for classifying various lung tissue types on high-resolution computed tomography. MRCNN employs Gabor-local binary pattern that introduces a good property in image analysis-invariance to image scales and rotations. In addition, we offer an approach to deal with the problems caused by imbalanced number of samples between different classes in most of the existing works, accomplished by changing the overlapping size between the adjacent patches. Experimental results on a public interstitial lung disease database show a superior performance of the proposed method to state of the art.

Comparing the performance of trained radiographers against experienced radiologists in the UK lung cancer screening (UKLS) trial

OBJECTIVE

To compare the performance of radiographers against that of radiologists for CT lung nodule detection in the UK Lung Cancer Screening (UKLS) pilot trial.

METHODS

Four radiographers, trained in CT nodule detection, and three radiologists were prospectively evaluated. 290 CTs performed for the UKLS were independently read by 2 radiologists and 2 radiographers. The reference standard comprised all radiologist-identified positive nodules after arbitration of discrepancies. For each radiographer and radiologist, relative sensitivity and average false positives (FPs) per case were compared for all cases read, as well as for subsets of cases read by each radiographer-radiologist combination (10 combinations).

RESULTS

599 nodules in 209/290 (72.1%) CT studies comprised the reference standard. The relative mean (±standard deviation) sensitivity of the four radiographers was 71.6 ± 8.5% compared with 83.3 ± 8.1% for the three radiologists. Radiographers were less sensitive and detected more FPs per case than radiologists in 7/10 and 8/10 radiographer-radiologist combinations, respectively (ranges of difference 11.2-33.8% and 0.4-2.6; p < 0.05). In 3/10 and 2/10 combinations, there was no difference in sensitivity and FPs per case between radiographers and radiologists. For nodules ≥100 mm(3) in volume or ≥5 mm in maximum diameter, radiographers were relatively less sensitive than radiologists in only 5/10 radiographer-radiologist combinations (range of difference 16.1-30.6%; p < 0.05) and not significantly different in the remaining 5/10 combinations.

CONCLUSION

Although overall radiographer performance was lower than that of experienced radiologists in this study, some radiographer performances were comparable with that of radiologists.

ADVANCES IN KNOWLEDGE

Overall, radiographers were less sensitive than radiologists reading the same CTs and also displayed higher average FP detections per case when compared with a reference standard derived from radiologist readings. However, some radiographers compared favourably with radiologists, especially when considering larger potentially clinically relevant nodules. Thus, while probably not sensitive enough to function as first readers, radiographers may still be able to fulfil the role of an assistant reader-that is, as a first or concurrent reader, who presents detected nodules for verification to a reading radiologist.

Using the CT features to differentiate invasive pulmonary adenocarcinoma from pre-invasive lesion appearing as pure or mixed ground-glass nodules

OBJECTIVE

To differentiate pre-invasive lesion from invasive pulmonary adenocarcinoma (IPA) appearing as ground-glass nodules (GGNs) using CT features.

METHODS

149 GGNs were enrolled in this study, with 74 pure GGNs (p-GGNs) and 75 mixed GGNs (m-GGNs). Firstly, univariate analysis was used to analyse the difference of CT features between pre-invasive lesion and IPA. Then, multivariate analysis was conducted to identify variables that could independently differentiate pre-invasive lesion from IPA. Receiver operating characteristic curve analysis was performed to evaluate the differentiating value of identified variables.

RESULTS

In the p-GGNs, multivariate analysis showed that the amount of blood vessels was an independent risk factor. Using the amount of blood vessels "≥1" as the diagnostic criterion, we could diagnose IPA with a sensitivity of 100%. Using the amount of blood vessels "=0" as the diagnostic criterion, we could diagnose pre-invasive lesions with a specificity of 100%. In the m-GGNs, multivariate analysis showed that the volume of solid portion (VSolid) and pleural indentation were two independent risk factors. One further model was constructed using these two variables: model = 2.508 × (VSolid + 1.407) × (pleural indentation - 1.016). Using the new model, improved diagnostic ability was achieved compared with using VSolid or pleural indentation alone.

CONCLUSION

The amount of blood vessels through the p-GGNs would be an important criterion during clinical management, while VSolid and pleural indentation seemed important for m-GGNs. Moreover, the new model could further improve the differentiating value for m-GGNs.

ADVANCES IN KNOWLEDGE

CT features are useful in differentiating pre-invasive lesion from IPA appearing as GGNs.

Computed tomography: revolutionizing the practice of medicine for 40 years

Computed tomography (CT) has had a profound effect on the practice of medicine. Both the spectrum of clinical applications and the role that CT has played in enhancing the depth of our understanding of disease have been profound. Although almost 90 000 articles on CT have been published in peer-reviewed journals over the past 40 years, fewer than 5% of these have been published in Radiology. Nevertheless, these almost 4000 articles have provided a basis for many important medical advances. By enabling a deepened understanding of anatomy, physiology, and pathology, CT has facilitated key advances in the detection and management of disease. This article celebrates this breadth of scientific discovery and development by examining the impact that CT has had on the diagnosis, characterization, and management of a sampling of major health challenges, including stroke, vascular diseases, cancer, trauma, acute abdominal pain, and diffuse lung diseases, as related to key technical advances in CT and manifested in Radiology.

Lung abscess mimicking lung cancer developed around staples in a patient with permanent tracheostoma

A 68-year-old male with a tracheostoma due to hypopharyngeal cancer was admitted because his chest computed tomography (CT) showed a small nodule in the right middle lobe. Following a partial resection of the right middle lobe, histopathological diagnosis of the resected sample was that of organizing pneumonia. Eleven months later, chest CT showed a mass with pleural indentation and spiculation in the right middle lobe. 18-Fluorodeoxyglucose-positron emission tomography showed significant accumulation in the middle lobe tumor mass shadow. The abnormal chest shadow that had developed around surgical staples suggested inadequate resection and tumor recurrence. As the abnormal radiological shadow was enlarging, middle lobectomy was carried out. Histological examination revealed that the tumor was a lung abscess without malignant features. This is a unique case of lung abscess mimicking lung cancer which developed around staples used during partial resection of the lung.

Screening for lung cancer

BACKGROUND

This is an updated version of the original review published in The Cochrane Library in 1999 and updated in 2004 and 2010. Population-based screening for lung cancer has not been adopted in the majority of countries. However it is not clear whether sputum examinations, chest radiography or newer methods such as computed tomography (CT) are effective in reducing mortality from lung cancer.

OBJECTIVES

To determine whether screening for lung cancer, using regular sputum examinations, chest radiography or CT scanning of the chest, reduces lung cancer mortality.

SEARCH METHODS

We searched electronic databases: the Cochrane Central Register of Controlled Trials (CENTRAL) (The Cochrane Library 2012, Issue 5), MEDLINE (1966 to 2012), PREMEDLINE and EMBASE (to 2012) and bibliographies. We handsearched the journal Lung Cancer (to 2000) and contacted experts in the field to identify published and unpublished trials.

SELECTION CRITERIA

Controlled trials of screening for lung cancer using sputum examinations, chest radiography or chest CT.

DATA COLLECTION AND ANALYSIS

We performed an intention-to-screen analysis. Where there was significant statistical heterogeneity, we reported risk ratios (RRs) using the random-effects model. For other outcomes we used the fixed-effect model.

MAIN RESULTS

We included nine trials in the review (eight randomised controlled studies and one controlled trial) with a total of 453,965 subjects. In one large study that included both smokers and non-smokers comparing annual chest x-ray screening with usual care there was no reduction in lung cancer mortality (RR 0.99, 95% CI 0.91 to 1.07). In a meta-analysis of studies comparing different frequencies of chest x-ray screening, frequent screening with chest x-rays was associated with an 11% relative increase in mortality from lung cancer compared with less frequent screening (RR 1.11, 95% CI 1.00 to 1.23); however several of the trials included in this meta-analysis had potential methodological weaknesses. We observed a non-statistically significant trend to reduced mortality from lung cancer when screening with chest x-ray and sputum cytology was compared with chest x-ray alone (RR 0.88, 95% CI 0.74 to 1.03). There was one large methodologically rigorous trial in high-risk smokers and ex-smokers (those aged 55 to 74 years with ≥ 30 pack-years of smoking and who quit ≤ 15 years prior to entry if ex-smokers) comparing annual low-dose CT screening with annual chest x-ray screening; in this study the relative risk of death from lung cancer was significantly reduced in the low-dose CT group (RR 0.80, 95% CI 0.70 to 0.92).

AUTHORS' CONCLUSIONS

The current evidence does not support screening for lung cancer with chest radiography or sputum cytology. Annual low-dose CT screening is associated with a reduction in lung cancer mortality in high-risk smokers but further data are required on the cost effectiveness of screening and the relative harms and benefits of screening across a range of different risk groups and settings.

Low-dose CT: technique, reading methods and image interpretation

The National Lung Cancer Screening Trial has recently demonstrated that screening of high-risk populations with the use of low-dose computed tomography (LDCT) reduces lung cancer mortality^[1]. Based on this encouraging result, the National Comprehensive Cancer Network guidelines recommended LDCT for selected patients at high risk of lung cancer^[2]. This suggests that an increasing number of CT screening examinations will be performed. The LDCT technique is relatively simple but some CT parameters are important and should be accurately defined in order to achieve good diagnostic quality and minimize the delivered dose. In addition, LDCT examinations are not as easy to read as they may initially appear; different approaches and tools are available for nodule detection and measurement. Moreover, the management of positive results can be a complex process and can differ significantly from routine clinical practice. Therefore this paper deals with the LDCT technique, reading methods and interpretation in lung cancer screening, particularly for those radiologists who have little experience of the technique.

Satisfaction of search from detection of pulmonary nodules in computed tomography of the chest

RATIONALE AND OBJECTIVES

We tested whether satisfaction of search (SOS) effects that occur in computed tomography (CT) examination of the chest on detection of native abnormalities are produced by the addition of simulated pulmonary nodules.

MATERIALS AND METHODS

Two experiments were conducted. In the first experiment, 70 CT examinations, half that demonstrated diverse, subtle abnormalities and half that demonstrated no native lesions, were read by 18 radiology residents and fellows under two experimental conditions: presented with and without pulmonary nodules. In a second experiment, many of the examinations were replaced to include more salient native abnormalities. This set was read by 14 additional radiology residents and fellows. In both experiments, detection of the natural abnormalities was studied. Receiver operating characteristic (ROC) curve areas for each reader-treatment combination were estimated using empirical and proper ROC models. Additional analyses focused on decision thresholds and visual search time on abnormality-free CT slice ranges. Institutional review board approval and informed consent from 32 participants were obtained.

RESULTS

Observers more often missed diverse native abnormalities when pulmonary nodules were added, but also made fewer false-positive responses. There was no change in ROC area, but decision criteria grew more conservative. The SOS effect on decision thresholds was accompanied by a reduction in search time on abnormality-free CT slice ranges.

CONCLUSION

The SOS effect in CT examination of the chest is similar to that found in contrast examination of the abdomen, involving induced visual neglect.

Computer-aided nodule detection and volumetry to reduce variability between radiologists in the interpretation of lung nodules at low-dose screening computed tomography

OBJECTIVE

The aim of this study was to evaluate whether a computer-aided diagnosis (CAD) system improves interobserver agreement in the interpretation of lung nodules at low-dose computed tomography (CT) screening for lung cancer.

MATERIALS AND METHODS

Baseline low-dose screening CT examinations from 134 participants enrolled in the National Lung Screening Trial were reviewed by 7 chest radiologists. All participants consented to the use of their deidentified images for research purposes. Screening results were classified as positive when noncalcified nodules larger than 4 mm in diameter were present. Follow-up evaluation was recommended according to the nodule diameter: 4 mm or smaller, more than 4 to 8 mm, and larger than 8 mm. When multiple nodules were present, recommendations were based on the largest nodule. Readers initially assessed the nodule presence visually and measured the average nodule diameter manually. Revision of their decisions after reviewing the CAD marks and size measurement was allowed. Interobserver agreement evaluated using multirater κ statistics was compared between initial assessment and that with CAD.

RESULTS

Multirater κ values for the positivity of the screening results and follow-up recommendations were improved from moderate (κ = 0.53-0.54) at initial assessment to good (κ = 0.66-0.67) after reviewing CAD results. The average percentage of agreement between reader pairs on the positivity of screening results and follow-up recommendations per case was also increased from 77% and 72% at initial assessment to 84% and 80% with CAD, respectively.

CONCLUSION

Computer-aided diagnosis may improve the reader agreement on the positivity of screening results and follow-up recommendations in the assessment of low-dose screening CT.

Endobronchial tumours in a campaign for early detection of bronchial cancer: Computed tomography versus endoscopy

OBJECTIVE

To study endobronchial cancers occurring in a population at high risk of bronchial cancer (history of surgically treated bronchial or ENT cancer in complete remission, and symptoms due to smoking) detected by annual volume CT scans and biannual fibroscopy.

MATERIAL AND METHODS

Two hundred and sixty-six patients were included in this single centre prospective study; 27 bronchopulmonary cancers were detected. Ten endobronchial cancers (37%) were identified by fibroscopy (nine invasive cancers and one carcinoma in situ) in 10 patients (nine men) (51-78 years old) nine of whom were smokers (dark tobacco: seven). The screening CTs were reappraised by two radiologists.

RESULTS

Three cancers out of 10 were detected by CT during the initial reading. The sensitivity of the reappraised CT was 80% with seven false positives. In five cases, the mean period between the first CT scan where the lesion was visible retrospectively, but not described, and the diagnostic fibroscopy was 463 days (213-808 days); two cancers were not visible in the CT scan. Seven curative treatments were undertaken.

CONCLUSION

In this population, the sensitivity of the initial reading of the CT scan for detecting endobronchial tumours was 30%, while 80% of the tumours were visible retrospectively, underlining the importance of careful analysis of the proximal bronchial tree.

Near-affine-invariant texture learning for lung tissue analysis using isotropic wavelet frames

We propose near-affine-invariant texture descriptors derived from isotropic wavelet frames for the characterization of lung tissue patterns in high-resolution computed tomography (HRCT) imaging. Affine invariance is desirable to enable learning of nondeterministic textures without a priori localizations, orientations, or sizes. When combined with complementary gray-level histograms, the proposed method allows a global classification accuracy of 76.9% with balanced precision among five classes of lung tissue using a leave-one-patient-out cross validation, in accordance with clinical practice.

Can computer assisted diagnosis (CAD) be used as a screening tool in the detection of pulmonary nodules when using 64-slice multidetector computed tomography?

OBJECTIVES

To evaluate (1) whether or not the addition of computer-assisted diagnosis (CAD) to 64-slice multidetector computed tomography (CT) can be used as a screening tool for detection of pulmonary nodules in routine CT chest examinations and (2) whether or not to advocate the incorporation of CAD as a screening tool into our daily practice.

MATERIALS AND METHODS

A retrospective cross-sectional analysis of 109 consecutive patients who had all undergone routine contrast-enhanced CT chest examinations for indications other than lung cancer at the Radiology Department of Aga Khan University Hospital, Karachi, between November 2010 and January 2011. All examinations were evaluated in terms of the detection of pulmonary nodules by a consultant radiologist and CAD (ImageChecker CT Algorithm R2 Technology) software. The ability of CAD software to detect pulmonary nodules was evaluated against the reference standard. In addition, a chest radiologist also calculated the number of pulmonary nodules. The sensitivity and specificity of the CAD software were calculated against the reference standard by using a 2 × 2 table. The Mann-Whitney U test was applied to compare the performances of CAD and the radiologist.

RESULTS

CAD detected 610 pulmonary nodules while the radiologist detected only 113. The reference standard declared 198 pulmonary nodules to be true nodules. CAD detected 95% of all true nodules (189/198), whereas the radiologist detected only 57% (113/198). In the detection of true pulmonary nodules, CAD had 98% sensitivity compared with the radiologist who had 57% sensitivity; the statistical difference between their performances had a P value <0.001.

CONCLUSION

Considering the high sensitivity of CAD to detect nearly all true pulmonary nodules, we advocate its application as a screening tool in all CT chest examinations for the early detection of pulmonary nodules and lung carcinoma.

A Study of Surgically Resected Peripheral Non-Small Cell Lung Cancer with a Tumor Diameter of 1.0 CM or Less

Background and Aims:

The widespread use of high resolution computed tomography has increased the number of small peripheral lung cancers. This study reviewed the clinicopathological features of the patients with non-small cell lung cancer (NSCLC) with a tumor diameter of 1 cm or less, in order to explore the adequate management of such small sized lung cancers.

Material and Methods:

This study was a retrospective analysis of consecutive 58 patients (5.3% out of 1095 patients) who underwent a complete resection for a peripheral NSCLC with a diameter of 1.0 cm or less. The clinical features and outcomes were compared with 203 patients with NSCLC with a diameter between 1.1 and 2.0 cm.

Results:

The mean age was 64.5 years and there were 26 males and 32 females. Clinical stage was IA in 57 (98%) and IIIA in 1. Lobectomy was performed in 39 patients, segmentectomy in nine, and nonanatomic wedge resection in ten. Two patients, who underwent systemic lymph node dissection, had mediastinal lymph node metastasis and were diagnosed as pathological stage IIIA; however they did not relapse after surgery. One patient with pathological stage IA papillary adenocarcinoma died due to brain metastases. The five-year overall survival rate and disease free survival rate was 95.0% and 95.3%, respectively. Patients with NSCLC of 1.0 cm or less showed significantly better survival than those with tumors measuring 1.1–2.0 cm in size (p = 0.048).

Discussion:

The indications for avoiding systemic lymph node dissection for operable NSCLC should not be based on the size of the tumor. A small-sized lung cancer might be surgically treated before the tumor enlarges to more than 1.0 cm in size.

Building a reference multimedia database for interstitial lung diseases

This paper describes the methodology used to create a multimedia collection of cases with interstitial lung diseases (ILDs) at the University Hospitals of Geneva. The dataset contains high-resolution computed tomography (HRCT) image series with three-dimensional annotated regions of pathological lung tissue along with clinical parameters from patients with pathologically proven diagnoses of ILDs. The motivations for this work is to palliate the lack of publicly available collections of ILD cases to serve as a basis for the development and evaluation of image-based computerized diagnostic aid. After 38 months of data collection, the library contains 128 patients affected with one of the 13 histological diagnoses of ILDs, 108 image series with more than 41l of annotated lung tissue patterns as well as a comprehensive set of 99 clinical parameters related to ILDs. The database is available for research on request and after signature of a license agreement.

Performance evaluation of a computer-aided detection algorithm for solid pulmonary nodules in low-dose and standard-dose MDCT chest examinations and its influence on radiologists

The aim of the study was to evaluate the performance of a computer-aided detection (CAD) algorithm in low-dose and full-dose multidetector-row CT (MDCT) of the thorax and its impact on radiologists' performance. Chest CT examinations of 77 patients were evaluated retrospectively for pulmonary nodules. All patients had undergone a 16-slice MDCT chest examination with a standard acquisition protocol. Artificial image noise was added to the raw data to simulate image acquisition at 10 mAs(eff.) The data were transferred to dedicated lung analysis software (LungCare) with a prototype CAD algorithm (LungCAD). CAD was applied to both dose settings. Images were read by a radiologist and a first-year resident with and without the software at both dose settings. All images were reviewed in consensus by the two radiologists to set the reference standard. Sensitivity results with respect to the reference standard were compared. No statistically significant differences in the detection rate for all pulmonary nodules could be found between low-dose and full-dose settings for the CAD software alone (p = 0.0065). Both radiologists displayed a statistically significant increase in sensitivity with the use of CAD (p<0.0001). In conclusion, CAD is beneficial in both low-dose and standard-dose settings. This may be beneficial in reducing false-negative diagnosis in lung cancer screening, standard chest examinations and the search for metastases.

[Usefulness of maximum intensity projections in low-radiation multislice CT lung cancer screening]

OBJECTIVE

To evaluate the diagnostic accuracy of non-overlapping 10-mm-thick axial maximum intensity projections (MIP) in the detection of pulmonary nodules in subjects participating in a lung cancer screening program (LCSP) using multislice computed tomography (MSCT) with a low dose of radiation.

MATERIAL AND METHODS

We evaluated 52 consecutive low-radiation MSCT studies in asymptomatic smokers included in an LCSP (1.25 mm axial images). Axial MIPs with 10mm slice thickness (30 images) were performed and evaluated retrospectively; readers were blind to the initial radiological report. All nodules detected were considered, regardless of their size or consistency. The standard of reference was determined by double reading and consensus for each nodule.

RESULTS

A total of 162 pulmonary nodules (mean size: 3.9 mm, sd: 1.7) were detected. MIP reconstruction detected 150 nodules (S = 92.6%). The initial radiological evaluation detected 108 nodules (S = 66.7%). MIP reconstruction detected 54 (33.3%) nodules that were not reported initially (mean size: 3.4 mm; sd: 1.2) but failed to detect 12 (7.4%) of the nodules reported initially (mean size: 2.91 mm; sd: 0.8). MIP detected all 35 nodules > or = 5 mm, (S =100), whereas the initial radiological evaluation only detected 27 (S = 77%). MIP reconstruction enabled more of the nodules to be detected than the 1.25-mm conventional axial slices (p < 0.01).

CONCLUSION

The introduction of non-overlapping 10-mm-thick axial MIP reconstructions in a low-radiation LCSP using MSCT enabled nodules more accurate and faster detection of pulmonary nodules in comparison with 1.25 mm conventional axial slices.

Computer-aided diagnosis of lung cancer and pulmonary embolism in computed tomography-a review

Computer-aided detection (CADe) and computer-aided diagnosis (CADx) have been important areas of research in the last two decades. Significant progress has been made in the area of breast cancer detection, and CAD techniques are being developed in many other areas. Recent advances in multidetector row computed tomography have made it an increasingly common modality for imaging of lung diseases. A thoracic examination using thin-section computed tomography contains hundreds of images. Detection of lung cancer and pulmonary embolism on computed tomographic (CT) examinations are demanding tasks for radiologists because they have to search for abnormalities in a large number of images, and the lesions can be subtle. If successfully developed, CAD can be a useful second opinion to radiologists in thoracic CT interpretation. In this review, we summarize the studies that have been reported in these areas, discuss some challenges in the development of CAD, and identify areas that deserve particular attention in future research.

Computer-aided detection of pulmonary nodules in computed tomography: analysis and review of the literature

PURPOSE

To evaluate diagnostic sensitivity of the pulmonary nodules computer-aided detection (CAD) in computed tomography. To analyze parameters that modify CAD performance. We made a critical analysis of the literature, and we described CAD sensitivity. Moreover, we compared CAD and CAD plus radiologist sensitivity in detection of pulmonary nodules, and we compared different acquisition techniques (thin slice vs thick slice and low dose vs normal dose).

MATERIALS AND METHODS

We used as major data sources the medical literature database of PubMed and MEDLINE, where we searched for articles in English language published from January 2001 to November 2006. We included studies that used spiral or multidetector row CT for CAD.

RESULTS

Twenty studies met the inclusion criteria containing a total of more than 827 patients and 2717 pulmonary nodules detected by CAD. We observed an overall sensitivity of 79% for the CAD and of 92% for CAD plus radiologist; CAD sensitivity was 80% and 74% for thin slice and thick slice protocols, respectively.

CONCLUSIONS

Results of our study suggest that CAD technique is an accurate tool in detection of pulmonary nodules, by working as useful second look for the physician. Sensitivity becomes higher by using it together with radiologist. Actually, the main limitation about the use of CAD to be solved is represented by the persistent high false-positive rate.

Discordance between CT and Angiography in the PIOPED II Study

PURPOSE

To retrospectively evaluate the causes of discordant computed tomographic (CT)-angiographic readings from the Prospective Investigation of Pulmonary Embolism Diagnosis, or PIOPED, II study.

MATERIALS AND METHODS

Institutional review board approval was obtained for this HIPAA-compliant study. Of 1036 patients suspected of having pulmonary embolism who were examined with CT, 226 underwent angiography; 206 patients had concordant results and 20 had discordant results according to two independent readers. Of these 20 patients, 10 were men and 10 were women (mean age, 49 years). Among the 20 studies with discordant results, central readers identified seven cases as negative and 13 as positive for pulmonary embolism at CT; these findings were reversed at angiography. Side-by-side comparisons of discordant studies were performed in consensus. The time between CT and angiography and all locations of pulmonary embolism vascular territory were recorded. The McNemar binomial test was used.

RESULTS

One patient had false-positive findings at angiography, 13 patients had false-negative findings at angiography, and two patients had false-negative findings at CT. Four patients had true-negative findings at CT; however, findings were positive for thrombus at angiography. The sensitivity for the detection of pulmonary embolism was 87% for CT and 32% for angiography (P=.007). The largest missed thrombus at angiography was subsegmental in eight patients, segmental in two patients, and lobar in three patients; at CT it was subsegmental in two patients. The mean time between CT and angiography was 40 hours+/-21 (standard deviation) (range, 10-97 hours).

CONCLUSION

In the interval between CT and angiography, thrombi can remain the same, resolve, develop, or result from angiography.

Evaluating computer-aided detection algorithms

Computer-aided detection (CAD) has been attracting extensive research interest during the last two decades. It is recognized that the full potential of CAD can only be realized by improving the performance and robustness of CAD algorithms and this requires good evaluation methodology that would permit CAD designers to optimize their algorithms. Free-response receiver operating characteristic (FROC) curves are widely used to assess CAD performance, however, evaluation rarely proceeds beyond determination of lesion localization fraction (sensitivity) at an arbitrarily selected value of nonlesion localizations (false marks) per image. This work describes a FROC curve fitting procedure that uses a recent model of visual search that serves as a framework for the free-response task. A maximum likelihood procedure for estimating the parameters of the model from free-response data and fitting CAD generated FROC curves was implemented. Procedures were implemented to estimate two figures of merit and associated statistics such as 95% confidence intervals and goodness of fit. One of the figures of merit does not require the arbitrary specification of an operating point at which to evaluate CAD performance. For comparison a related method termed initial detection and candidate analysis was also implemented that is applicable when all suspicious regions are reported. The two methods were tested on seven mammography CAD data sets and both yielded good to excellent fits. The search model approach has the advantage that it can potentially be applied to radiologist generated free-response data where not all suspicious regions are reported, only the ones that are deemed sufficiently suspicious to warrant clinical follow-up. This work represents the first practical application of the search model to an important evaluation problem in diagnostic radiology. Software based on this work is expected to benefit CAD developers working in diverse areas of medical imaging.

[Computer-aided detection of lung nodules on thin collimation MDCT: impact on radiologists' performance]

OBJECTIVES

Evaluate the improvement in detecting lung nodules when using multidetector CT (MDCT) computer-assisted diagnosis (CAD).

MATERIAL AND METHODS

Three radiologists (R1, R2, R3) with different levels of experience independently interpreted 30 MDCT examinations of the thorax taken for screening purposes, first without and then with CAD. The diagnosis was established by two of the three radiologists interpreting the images together, assisted by the CAD.

RESULTS

The consensus reading identified 133 nodules, 61 (46%) of which were 4 mm or larger. The sensitivity values in the detection of nodules before and after using the CAD were 54% and 80% (R1), 38% and 71% (R2), and 70% and 88% (R3), respectively. When considering only the nodules that were 4 mm or larger, the sensitivity values varied before and after using the CAD, from 62% to 95% (R1), from 41% to 84% (R2), and from 74% to 92% (R3). By combining two by two the three radiologists' results obtained without the CAD, the sensitivity values were 65%, 83%, and 77%, respectively, for all the nodules, and 70%, 85%, and 77% for the nodules that were 4 mm or larger. The CAD induced a total of 105 false-positive results, with a mean of 3.5 per examination.

CONCLUSION

The lung nodules missed by the radiologist can be detected if the CAD is used as a second reader. The CAD can be at least as beneficial as the use of a second independent reader.

Lung metastases detection in CT images using 3D template matching

The aim of this study is to demonstrate a novel, fully automatic computer detection method applicable to metastatic tumors to the lung with a diameter of 4-20 mm in high-risk patients using typical computed tomography (CT) scans of the chest. Three-dimensional (3D) spherical tumor appearance models (templates) of various sizes were created to match representative CT imaging parameters and to incorporate partial volume effects. Taking into account the variability in the location of CT sampling planes cut through the spherical models, three offsetting template models were created for each appearance model size. Lung volumes were automatically extracted from computed tomography images and the correlation coefficients between the subregions around each voxel in the lung volume and the set of appearance models were calculated using a fast frequency domain algorithm. To determine optimal parameters for the templates, simulated tumors of varying sizes and eccentricities were generated and superposed onto a representative human chest image dataset. The method was applied to real image sets from 12 patients with known metastatic disease to the lung. A total of 752 slices and 47 identifiable tumors were studied. Spherical templates of three sizes (6, 8, and 10 mm in diameter) were used on the patient image sets; all 47 true tumors were detected with the inclusion of only 21 false positives. This study demonstrates that an automatic and straightforward 3D template-matching method, without any complex training or postprocessing, can be used to detect small lung metastases quickly and reliably in the clinical setting.

Profiling tumor-associated antibodies for early detection of non-small cell lung cancer

BACKGROUND

A blood test for non-small cell lung cancer (NSCLC) may be a valuable tool for use in a comprehensive lung cancer screening strategy. Here we report the potential of autoantibody profiling to detect early-stage and occult NSCLC.

METHODS

T7-phage NSCLC cDNA libraries were screened with patient plasma to identify phage-expressed proteins recognized by tumor-associated antibodies. Two hundred twelve immunogenic phage-expressed proteins, identified from 4000 clones, were statistically ranked for their individual reactivity with 23 stage I cancer patient and 23 risk-matched control samples. All 46 samples were used as a training set to define a combination of markers that were best able to distinguish patient from control samples; this set of classifiers was then examined using leave-one-out cross-validation. Markers were then used to predict probability of disease in 102 samples from the Mayo Clinic CT Screening Trial (six prevalence cancer samples, 40 drawn 1 to 5 years before diagnosis, and 56 risk-matched controls).

RESULTS

Measurements of the five most predictive antibody markers in 46 cases and controls were combined in a logistic regression model that yielded area under the receiver operating characteristics curve of 0.99; leave-one-out validation achieved 91.3% sensitivity and 91.3% specificity. In testing this marker set with samples from the Mayo Clinic Lung Screening Trial, we correctly predicted six of six prevalence cancers, 32 of 40 cancers from samples drawn 1 to 5 years before radiographic detection on incidence screening, and 49 of 56 risk-matched controls.

CONCLUSIONS

Antibody profiling may be a useful tool for early detection of NSCLC.

Small Pulmonary Nodules: Effect of Two Computer-aided Detection Systems on Radiologist Performance

PURPOSE

To prospectively compare the effects of two computer-aided detection (CAD) systems on the detection of small pulmonary nodules at multi-detector row computed tomography (CT) by using a consensus panel decision as the reference standard.

MATERIALS AND METHODS

Institutional review board approval and informed consent were obtained. Multi-detector row CT scans were randomly chosen and prospectively evaluated in 25 patients. Two dedicated CAD systems-ImageChecker CT (R2 Technologies, Sunnyvale, Calif) and Nodule Enhanced Viewing (NEV) (Siemens Medical Solutions, Forchheim, Germany)-were used. Results were interpreted by three radiologists with 1, 3, and 6 years of experience. Images were evaluated without and with CAD software. The reference standard was assessed by a consensus panel consisting of all three radiologists and an adjudicator with 8 years of experience.

RESULTS

A total of 116 pulmonary nodules (average diameter, 3.4 mm; average volume, 32.05 mm3) were found in all data sets during consensus interpretation, which included findings from the CAD software and all radiologists. Overall sensitivity was 73% with ImageChecker CT and 75% with NEV. Overall sensitivity without CAD was 68% for radiologist 1, 78% for radiologist 2, and 82% for radiologist 3. With ImageChecker CT, sensitivity increased to 79% for radiologist 1, 90% for radiologist 2, and 84% for radiologist 3. With NEV, sensitivity increased to 79% for radiologist 1, 90% for radiologist 2, and 86% for radiologist 3. The average number of false-positive findings was six (range, 0-14) with ImageChecker CT and eight (range, 0-22) with NEV.

CONCLUSION

Radiologist performance in the interpretation of multi-detector row CT scans can be improved by using CAD systems, with a reduction in the number of false-negative diagnoses. No statistically significant difference in sensitivity was found between the two CAD systems.

Outcome in patients with lung cancer invisible on chest roentgenograms but detected only by helical computed tomography

The aim of this study was to evaluate the prognosis for patients with lung cancer detected by helical CT but not by CXR. One hundred and thirty-seven asymptomatic patients with lung cancer diagnosed by annual mass screening of the chest were enrolled over a 7-year period. Five-year survival rates in patients with lung cancer detected only by helical CT (n = 19: CT-only detection group) and in patients with lung cancer visible by both CXR and helical CT (n = 118: control group) were evaluated, and clinical variables were examined as possible predictors of survival time using the Cox proportional-hazards model. There was a significant difference between the 5-year survival rates in the CT-only detection group and in the control group (80% vs. 39%, log rank: P = 0.0171). The risk of death decreased 77% in CT-only detectable lung cancer (hazard ratio: 0.219, 95% confidence interval: 0.057-0.845, P = 0.0275). Lung cancer could not be seen by CXR because nodules were small or faint (n = 11) or overlapping a shadow of thoracic components (n = 8). The percentage of subsolid nodules (classified as either part-solid or non-solid nodules) was higher in the subgroup with small or faint nodules (82% vs. 25%, P = 0.0423). Helical CT has the ability to detect early lung cancer before the small or faint nodules increase to a size visible on CXR, and patients with lung cancer detected only by helical CT have a better prognosis.

Coronary Artery Imaging with Contrast-Enhanced MDCT: Extracardiac Findings

OBJECTIVE

The purpose of our study was to evaluate the incidence of extracardiac findings on contrast-enhanced MDCT of the coronary arteries and to assess the effect of different field-of-view settings.

SUBJECTS AND METHODS

Patients with suspected coronary artery disease (n = 166) were examined with contrast-enhanced MDCT (16 x 0.75 mm focused on the heart) during injection of contrast material (80 mL injected at a rate of 4 mL/sec) followed by saline (20 mL injected at 4 mL/sec). Retrospectively gated images were reconstructed at a 1-mm slice thickness and a 0.5-mm increment with isotropic voxels of 1 mm3. Images were reviewed for extracardiac findings, which were then classified as none, minor, or major with respect to their impact on patient management and treatment. In a different group of patients (n = 20), chest scans (16 x 1.5 mm) were used for measuring volumes of displayed body structures on wholechest scans, coronary artery MDCT images, and coronary artery MDCT images reconstructed with the maximum field of view.

RESULTS

Extracardiac findings were detected in 41 patients (24.7%). Findings were classified as minor (19.9%) or major (4.8%). Among the major findings, which had an immediate impact on patient management and treatment, were bronchial carcinoma and pulmonary emboli. Volume analysis revealed that 35.5% of the total chest volume was displayed on dedicated coronary artery MDCT focused on the heart, whereas 70.3% of the chest was visible when coronary artery MDCT raw data were reconstructed with the maximal field of view (p < 0.001).

CONCLUSION

Coronary artery MDCT can reveal important findings and disease in extracardiac structures. Thus, the entire examination should be reconstructed with the maximum field of view and should be reviewed by a qualified radiologist.

CT and MRI of Lung Cancer

Computed tomography (CT) is still the cornerstone of imaging studies in the preoperative staging and post- therapeutic evaluation of lung cancer. The most recent developments in multidetector technology have dramatically improved the temporal and spatial resolution of CT. In the mean time, magnetic resonance imaging (MRI) has not become a routine examination in lung imaging and is today only used as a problem-solving tool in patients in whom CT remains equivocal. This article will describe the current tools developed in the multidetector CT era for evaluating the lung, and state-of-the-art MR examination of the chest. Then, the role of CT and MRI in nodule detection, the distinction between benign and malignant nodules, and the benefit of CT and MRI in the staging and post-therapeutic evaluation of lung cancer will be covered.

Pulmonary nodule detection in CT images with quantized convergence index filter

A novel filter termed quantized convergence index filter (QCI filter) that is capable of enhancing the conspicuity of rounded lesions is proposed as part of a CAD (computer-aided diagnosis) scheme for detecting pulmonary nodules in computed tomography (CT) images. In this filter and its predecessor, the convergence index filter (CI filter), the output at a pixel represents the degree of convergence toward the pixel shown by the directions of gray-level gradients at surrounding pixels. The QCI filter and the CAD scheme were evaluated using five clinical datasets containing 50 nodules. With the support region of 9 x 9 pixels, the QCI filter showed more selective response to the nodules than the CI filter. In the CAD scheme, intermediate nodule candidates are generated based on the QCI filter output and then classified using linear discriminant analysis of eight features that are attributed to each intermediate nodule candidate. The QCI filter output level itself was used as one of the features. The scheme achieved a sensitivity of 90% with 1.67 false positives per slice. The QCI filter output level was most effective among the features in correctly classifying intermediate nodule candidates. The QCI filter is promising as a tool of preprocessing for automated pulmonary nodule detection in CT images.

Development of a ligand-directed approach to study the pathogenesis of invasive aspergillosis

Invasive aspergillosis is a leading cause of infectious death in immunosuppressed patients. Here, we adapted a phage display library-based selection to screen and identify binding peptides to the surface of Aspergillus fumigatus conidia and hyphae. We identified a peptide (sequence CGGRLGPFC) that reliably binds to the surface of Aspergillus fumigatus hyphae. Binding was not Aspergillus strain specific, as it was also observed in hyphae of other Aspergillus clinical isolates. Furthermore, CGGRLGPFC-displaying phage targets Aspergillus fumigatus hyphae on formalin-fixed paraffin-embedded histopathology sections of lung tissue recovered from mice with invasive pulmonary aspergillosis. This approach may yield reagents such as peptidomimetics for novel diagnostic and therapeutic interventions in invasive aspergillosis.

Screening for lung cancer: New horizons?

Lung cancer remains the leading cause of cancer-related deaths in the world. At present, the only high rate of cure therapy is surgical resection at early stage of disease. Early detection could potentially decrease lung cancer mortality suggesting that this cancer should be a good candidate for screening. Results of trials involving chest X-ray, sputum cytology and low-dose computed tomography (CT) are discussed here. The latter tool offers advantages over chest X-ray, but final results from controlled well conducted trials are necessary before the real utility of CT mass screening can be determined. Further approaches to secondary prevention such as screening with positron emission tomography (PET), autofluorescence bronchoscopy and biomarkers hold great promise for the future.

Computer-aided detection of peripheral lung cancers missed at CT: ROC analyses without and with localization

PURPOSE

To retrospectively evaluate whether a difference-image computer-aided detection (CAD) scheme can help radiologists detect peripheral lung cancers missed at low-dose computed tomography (CT).

MATERIALS AND METHODS

Institutional review board approval and informed patient and observer consent were obtained. Seventeen patients (eight men and nine women; mean age, 60 years) with a missed peripheral lung cancer and 10 control subjects (five men and five women; mean age, 63 years) without cancer at low-dose CT were included in an observer study. Fourteen radiologists were divided into two groups on the basis of different image display formats: Six radiologists (group 1) reviewed CT scans with a multiformat display, and eight radiologists (group 2) reviewed images with a "stacked" cine-mode display. The radiologists, first without and then with the CAD scheme, indicated their confidence level regarding the presence (or absence) of cancer and the most likely position of a lesion on each CT scan. Receiver operating characteristic (ROC) curves were calculated without and with localization to evaluate the observers' performance.

RESULTS

With the CAD scheme, the average area under the ROC curve improved from 0.763 to 0.854 for all radiologists (P = .002), from 0.757 to 0.862 for group 1 (P = .04), and from 0.768 to 0.848 for group 2 (P = .01). The average sensitivity in the detection of 17 cancers improved from 52% (124 of 238 observations) to 68% (163 of 238 observations) for all radiologists (P < .001), from 49% (50 of 102 observations) to 71% (72 of 102 observations) for group 1 (P = .02), and from 54% (74 of 136 observations) to 67% (91 of 136 observations) for group 2 (P = .006). The localization ROC curve also improved.

CONCLUSION

Lung cancers missed at low-dose CT were very difficult to detect, even in an observer study. The use of CAD, however, can improve radiologists' performance in the detection of these subtle cancers.

Computer-aided diagnosis as a second reader: spectrum of findings in CT studies of the chest interpreted as normal

STUDY OBJECTIVES

To assess the performance of an automated computer-aided detection (CAD) system as a second reader on chest CT studies interpreted as normal at routine clinical interpretation.

DESIGN

Chest CT studies were processed using a prototype CAD system for automated detection of lung lesions. Three experienced radiologists analyzed each CAD finding and confirmed or dismissed the marked image features as lung lesions. Noncalcified, focal lung lesions were classified according to size as being of high (> or = 10 mm), intermediate (5 to 9 mm), or low (< or = 4 mm) significance.

SETTING

Two sub-specialized academic tertiary referral centers in the United States and Germany.

PATIENTS

Chest CT studies were performed in 100 patients, with results initially reported as normal at clinical double reading. Indications for chest CT were suspected pulmonary embolism (PE) [n = 33], lung cancer screening in a high-risk population (n = 28), or follow-up for a cancer history (n = 39).

INTERVENTIONS

Reevaluation of all chest CT studies for focal lung lesions with the CAD system as a second reader.

MEASUREMENTS

Prevalence and spectrum of lung lesions missed at routine clinical interpretation but found by the CAD system.

RESULTS

In 33% (33 of 100 patients), CAD detected significant lung lesions that were not previously reported. Fifty-three significant lesions were detected (mean, 1.6 lesions per case), of which 5 lesions (9.4%) were of high significance, 21 lesions (39.6%) were of intermediate significance, and 27 lesions (50.9%) were of low significance. In the PE group, the lung cancer screening group, and the group with a cancer history, four patients (12.1%), six patients (21.4%), and nine patients (23.1%), respectively, had focal lung lesions of high and/or intermediate significance. The false-positive rate of the CAD system was an average of 1.25 per case (range, 0 to 11).

CONCLUSIONS

Significant lung lesions are frequently missed at routine clinical interpretation of chest CT studies but may be detected if CAD is used as an additional reader.

Small incidental pulmonary nodules: how useful is short-term interval CT follow-up?

PURPOSE

To determine whether short-term (<6 month) follow-up imaging by thoracic CT is necessary to evaluate small (< or =4 mm) noncalcified nodules (NCN) in patients with no history of malignancy or acute lung disease.

MATERIALS AND METHODS

We reviewed serial thoracic helical CT scans between 1999-2000 obtained for the evaluation or follow-up imaging of small NCNs. CT scans were performed at 5-mm collimation. Patients were excluded if they had a history of neoplasm, infection, pulmonary fibrosis, or immune deficiency; also excluded if nodule(s) > or =5 mm at intake, or no follow-up scan within 1 year. NCNs were evaluated for number, size, configuration, and change in size over time. Exact 95% Confidence Intervals were used to estimate chances of nodule growth in 3-, 6-, and 12- month intervals.

RESULTS

A total of 1826 patients received a CT for NCNs. Four hundred fourteen patients met inclusion criteria (221 women, 193 men; mean age, 65.6 years). Eighty-five patients had a single NCN, 329 had multiple NCNs. One hundred seventy-three patients had additional > or =5 mm nodules. One hundred twenty-seven patients were lost to follow up within the 1-year period. Eight nodules cleared. None of the < or =4 mm NCNs grew on follow-up imaging within 12 months. Three patients developed lung cancer in other nodules > or =5 mm (5-10 mm). These nodules grew on follow up intervals of 3-13 months. One patient had a 19 mm benign hamartoma that grew in 9 months. The calculated chance that a NCN < or =4 mm will grow within 3, 6, and 12 months (95% CI) is < or =0.89%, 1.01%, and 1.28%, respectively.

CONCLUSION

The chance of growth in < or =4 mm NCNs in a 3- to 6-month period in patients with no previous history of malignancy or immune disorder is small; therefore, short-term follow-up imaging (<12 month) for nodules < or =4 mm is not necessary.