Non-small-cell lung cancer: results of the New York screening program

Separation of bones from soft tissue in chest radiographs: Anatomy-specific orientation-frequency-specific deep neural network convolution

PURPOSE

Lung nodules that are missed by radiologists as well as by computer-aided detection (CAD) systems mostly overlap with ribs and clavicles. Removing the bony structures would result in better visualization of undetectable lesions. Our purpose in this study was to develop a virtual dual-energy imaging system to separate ribs and clavicles from soft tissue in chest radiographs.

METHODS

We developed a mixture of anatomy-specific, orientation-frequency-specific (ASOFS) deep neural network convolution (NNC) experts. Anatomy-specific (AS) NNC was designed to separate the bony structures from soft tissue in different lung segments. While an AS design was proposed previously under our massive-training artificial neural networks (MTANN) framework, in this work, we newly mathematically defined an AS experts model as well as its learning and inference strategies in a probabilistic deep-learning framework. In addition, in combination with our AS experts design, we newly proposed the orientation-frequency-specific (OFS) NNC models to decompose bone and soft-tissue structures into specific orientation-frequency components of different scales using a multi-resolution decomposition technique. We trained multiple NNC models, each of which is an expert for a specific orientation-frequency component in a particular anatomic segment. Perfect reconstruction discrete wavelet transform was used for OFS decomposition/reconstruction, while we introduced a soft-gating layer to merge the predictions of AS NNC experts. To train our model, we used the bone images obtained from a dual-energy system as the target (or teaching) images while the standard chest radiographs were used as the input to our model. The training, validation, and test were performed in a nested two-fold cross-validation manner.

RESULTS

We used a database of 118 chest radiographs with pulmonary nodules to evaluate our NNC scheme. In order to evaluate our scheme, we performed quantitative and qualitative evaluation of the predicted bone and soft-tissue images from our model as well as the ones of a state-of-the-art technique where the "gold-standard" dual-energy bone and soft-tissue images were used as the reference images. Both quantitative and qualitative evaluations demonstrated that our ASOFS NNC was superior to the state-of-the-art bone-suppression technique. Particularly, our scheme was better able to maintain the conspicuity of nodules and lung vessels, comparing to the reference technique, while it separated ribs and clavicles from soft tissue. Comparing to a state-of-the-art bone suppression technique, our bone images had substantially higher (t-test; P < 0.01) similarity, in terms of structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR), to the "gold-standard" dual-energy bone images.

CONCLUSIONS

Our deep ASOFS NNC scheme can decompose chest radiographs into their bone and soft-tissue images accurately, offering the improved conspicuity of lung nodules and vessels, and therefore would be useful for radiologists as well as CAD systems in detecting lung nodules in chest radiographs.

Unsupervised segmentation of lung fields in chest radiographs using multiresolution fractal feature vector and deformable models

Segmenting lung fields in a chest radiograph is essential for automatically analyzing an image. We present an unsupervised method based on multiresolution fractal feature vector. The feature vector characterizes the lung field region effectively. A fuzzy c-means clustering algorithm is then applied to obtain a satisfactory initial contour. The final contour is obtained by deformable models. The results show the feasibility and high performance of the proposed method. Furthermore, based on the segmentation of lung fields, the cardiothoracic ratio (CTR) can be measured. The CTR is a simple index for evaluating cardiac hypertrophy. After identifying a suspicious symptom based on the estimated CTR, a physician can suggest that the patient undergoes additional extensive tests before a treatment plan is finalized.

Magnetic resonance imaging for lung cancer screen

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

Evidence based imaging strategies for solitary pulmonary nodule

Solitary pulmonary nodule (SPN) is defined as a rounded opacity ≤3 cm in diameter surrounded by lung parenchyma. The majority of smokers who undergo thin-section CT have SPNs, most of which are smaller than 7 mm. In the past, multiple follow-up examinations over a two-year period, including CT follow-up at 3, 6, 12, 18, and 24 months, were recommended when such nodules are detected incidentally. This policy increases radiation burden for the affected population. Nodule features such as shape, edge characteristics, cavitation, and location have not yet been found to be accurate for distinguishing benign from malignant nodules. When SPN is considered to be indeterminate in the initial exam, the risk factor of the patients should be evaluated, which includes patients' age and smoking history. The 2005 Fleischner Society guideline stated that at least 99% of all nodules 4 mm or smaller are benign; when nodule is 5-9 mm in diameter, the best strategy is surveillance. The timing of these control examinations varies according to the nodule size (4-6, or 6-8 mm) and the type of patients, specifically at low or high risk of malignancy concerned. Noncalcified nodules larger than 8 mm diameter bear a substantial risk of malignancy, additional options such as contrast material-enhanced CT, positron emission tomography (PET), percutaneous needle biopsy, and thoracoscopic resection or videoassisted thoracoscopic resection should be considered.

New methods for using computer-aided detection information for the detection of lung nodules on chest radiographs

OBJECTIVE

To investigate two new methods of using computer-aided detection (CAD) system information for the detection of lung nodules on chest radiographs. We evaluated an interactive CAD application and an independent combination of radiologists and CAD scores.

METHODS

300 posteroanterior and lateral digital chest radiographs were selected, including 111 with a solitary pulmonary nodule (average diameter, 16 mm). Both nodule and control cases were verified by CT. Six radiologists and six residents reviewed the chest radiographs without CAD and with CAD (ClearRead +Detect™ 5.2; Riverain Technologies, Miamisburg, OH) in two reading sessions. The CAD system was used in an interactive manner; CAD marks, accompanied by a score of suspicion, remained hidden unless the location was queried by the radiologist. Jackknife alternative free response receiver operating characteristics multireader multicase analysis was used to measure detection performance. Area under the curve (AUC) and partial AUC (pAUC) between a specificity of 80% and 100% served as the measure for detection performance. We also evaluated the results of a weighted combination of CAD scores and reader scores, at the location of reader findings.

RESULTS

AUC for the observers without CAD was 0.824. No significant improvement was seen with interactive use of CAD (AUC = 0.834; p = 0.15). Independent combination significantly improved detection performance (AUC = 0.834; p = 0.006). pAUCs without and with interactive CAD were similar (0.128), but improved with independent combination (0.137).

CONCLUSION

Interactive CAD did not improve reader performance for the detection of lung nodules on chest radiographs. Independent combination of reader and CAD scores improved the detection performance of lung nodules.

ADVANCES IN KNOWLEDGE

(1) Interactive use of currently available CAD software did not improve the radiologists' detection performance of lung nodules on chest radiographs. (2) Independently combining the interpretations of the radiologist and the CAD system improved detection of lung nodules on chest radiographs.

Separation of bones from chest radiographs by means of anatomically specific multiple massive-training ANNs combined with total variation minimization smoothing

Most lung nodules that are missed by radiologists as well as computer-aided detection (CADe) schemes overlap with ribs or clavicles in chest radiographs (CXRs). The purpose of this study was to separate bony structures such as ribs and clavicles from soft tissue in CXRs. To achieve this, we developed anatomically specific multiple massive-training artificial neural networks (MTANNs) combined with total variation (TV) minimization smoothing and a histogram-matching-based consistency improvement method. The anatomically specific multiple MTANNs were designed to separate bones from soft tissue in different anatomic segments of the lungs. Each of the MTANNs was trained with the corresponding anatomic segment in the teaching bone images. The output segmental images from the multiple MTANNs were merged to produce an entire bone image. TV minimization smoothing was applied to the bone image for reduction of noise while preserving edges. This bone image was then subtracted from the original CXR to produce a soft-tissue image where bones were separated out. This new method was compared with conventional MTANNs with a database of 110 CXRs with nodules. Our new anatomically specific MTANNs separated rib edges, ribs close to the lung wall, and the clavicles from soft tissue in CXRs to a substantially higher level than did the conventional MTANNs, while the conspicuity of lung nodules and vessels was maintained. Thus, our technique for bone-soft-tissue separation by means of our new MTANNs would be potentially useful for radiologists as well as CADe schemes in detection of lung nodules on CXRs.

Cognitive and system factors contributing to diagnostic errors in radiology

OBJECTIVE

In this article, we describe some of the cognitive and system-based sources of detection and interpretation errors in diagnostic radiology and discuss potential approaches to help reduce misdiagnoses.

CONCLUSION

Every radiologist worries about missing a diagnosis or giving a false-positive reading. The retrospective error rate among radiologic examinations is approximately 30%, with real-time errors in daily radiology practice averaging 3-5%. Nearly 75% of all medical malpractice claims against radiologists are related to diagnostic errors. As medical reimbursement trends downward, radiologists attempt to compensate by undertaking additional responsibilities to increase productivity. The increased workload, rising quality expectations, cognitive biases, and poor system factors all contribute to diagnostic errors in radiology. Diagnostic errors are underrecognized and underappreciated in radiology practice. This is due to the inability to obtain reliable national estimates of the impact, the difficulty in evaluating effectiveness of potential interventions, and the poor response to systemwide solutions. Most of our clinical work is executed through type 1 processes to minimize cost, anxiety, and delay; however, type 1 processes are also vulnerable to errors. Instead of trying to completely eliminate cognitive shortcuts that serve us well most of the time, becoming aware of common biases and using metacognitive strategies to mitigate the effects have the potential to create sustainable improvement in diagnostic errors.

Bone suppressed images improve radiologists' detection performance for pulmonary nodules in chest radiographs

OBJECTIVES

To assess the effect of bone suppression imaging on observer performance in detecting lung nodules in chest radiographs.

MATERIALS AND METHODS

Posteroanterior (PA) and lateral digital chest radiographs of 111 (average age 65) patients with a CT proven solitary nodule (median diameter 15 mm), and 189 (average age 63) controls were read by 5 radiologists and 3 residents. Conspicuity of nodules on the radiographs was classified in obvious (n = 32), moderate (n = 32), subtle (n = 29) and very subtle (n = 18). Observers read the PA and lateral chest radiographs without and with an additional PA bone suppressed image (BSI) (ClearRead Bone Suppression 2.4, Riverain Technologies, Ohio) within one reading session. Multi reader multi case (MRMC) receiver operating characteristics (ROC) were used for statistical analysis.

RESULTS

ROC analysis showed improved detection with use of BSI compared to chest radiographs alone (AUC = 0.883 versus 0.855; p = 0.004). Performance also increased at high specificities exceeding 80% (pAUC = 0.136 versus 0.124; p = 0.0007). Operating at a specificity of 90%, sensitivity increased with BSI from 66% to 71% (p = 0.0004). Increase of detection performance was highest for nodules with moderate and subtle conspicuity (p = 0.02; p = 0.03).

CONCLUSION

Bone suppressed images improve radiologists' detection performance for pulmonary nodules, especially for those of moderate and subtle conspicuity.

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.

Machine Learning in Computer-aided Diagnosis of the Thorax and Colon in CT: A Survey

Computer-aided detection (CADe) and diagnosis (CAD) has been a rapidly growing, active area of research in medical imaging. Machine leaning (ML) plays an essential role in CAD, because objects such as lesions and organs may not be represented accurately by a simple equation; thus, medical pattern recognition essentially require "learning from examples." One of the most popular uses of ML is the classification of objects such as lesion candidates into certain classes (e.g., abnormal or normal, and lesions or non-lesions) based on input features (e.g., contrast and area) obtained from segmented lesion candidates. The task of ML is to determine "optimal" boundaries for separating classes in the multidimensional feature space which is formed by the input features. ML algorithms for classification include linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), multilayer perceptrons, and support vector machines (SVM). Recently, pixel/voxel-based ML (PML) emerged in medical image processing/analysis, which uses pixel/voxel values in images directly, instead of features calculated from segmented lesions, as input information; thus, feature calculation or segmentation is not required. In this paper, ML techniques used in CAD schemes for detection and diagnosis of lung nodules in thoracic CT and for detection of polyps in CT colonography (CTC) are surveyed and reviewed.

A review of computer-aided diagnosis in thoracic and colonic imaging

Medical imaging has been indispensable in medicine since the discovery of x-rays. Medical imaging offers useful information on patients' medical conditions and on the causes of their symptoms and diseases. As imaging technologies advance, a large number of medical images are produced which physicians/radiologists must interpret. Thus, computer aids are demanded and become indispensable in physicians' decision making based on medical images. Consequently, computer-aided detection and diagnosis (CAD) has been investigated and has been an active research area in medical imaging. CAD is defined as detection and/or diagnosis made by a radiologist/physician who takes into account the computer output as a "second opinion". In CAD research, detection and diagnosis of lung and colorectal cancer in thoracic and colonic imaging constitute major areas, because lung and colorectal cancers are the leading and second leading causes, respectively, of cancer deaths in the U.S. and also in other countries. In this review, CAD of the thorax and colon, including CAD for detection and diagnosis of lung nodules in thoracic CT, and that for detection of polyps in CT colonography, are reviewed.

Missed lung cancer on chest radiography and computed tomography

Missed lung cancer raises an important medicolegal issue and contributes to one of the most common causes for malpractice actions against radiologists. Lung cancer may be missed on either chest radiography or computed tomography. Although most malpractice cases involve lesions overlooked on the former, a small and increasing portion of cases are related to chest computed tomography scan. Factors contributing to overlooked lung cancer can be attributed to observer performance, lesion characteristics, and technical considerations.

Observer training for computer-aided detection of pulmonary nodules in chest radiography

OBJECTIVES

To assess whether short-term feedback helps readers to increase their performance using computer-aided detection (CAD) for nodule detection in chest radiography.

METHODS

The 140 CXRs (56 with a solitary CT-proven nodules and 84 negative controls) were divided into four subsets of 35; each were read in a different order by six readers. Lesion presence, location and diagnostic confidence were scored without and with CAD (IQQA-Chest, EDDA Technology) as second reader. Readers received individual feedback after each subset. Sensitivity, specificity and area under the receiver-operating characteristics curve (AUC) were calculated for readings with and without CAD with respect to change over time and impact of CAD.

RESULTS

CAD stand-alone sensitivity was 59 % with 1.9 false-positives per image. Mean AUC slightly increased over time with and without CAD (0.78 vs. 0.84 with and 0.76 vs. 0.82 without CAD) but differences did not reach significance. The sensitivity increased (65 % vs. 70 % and 66 % vs. 70 %) and specificity decreased over time (79 % vs. 74 % and 80 % vs. 77 %) but no significant impact of CAD was found.

CONCLUSION

Short-term feedback does not increase the ability of readers to differentiate true- from false-positive candidate lesions and to use CAD more effectively.

KEY POINTS

• Computer-aided detection (CAD) is increasingly used as an adjunct for many radiological techniques. • Short-term feedback does not improve reader performance with CAD in chest radiography. • Differentiation between true- and false-positive CAD for low conspicious possible lesions proves difficult. • CAD can potentially increase reader performance for nodule detection in chest radiography.

Small peripheral lung adenocarcinoma: CT and histopathologic characteristics and prognostic implications

Since the introduction of computed tomography (CT), detection of small lung cancer, especially small peripheral adenocarcinoma, is common. Recently, the morphological characteristics, including thin-section CT and pathologic findings, and prognosis of small peripheral lung adenocarcinomas have been studied extensively. The radiologic and microscopic findings correlate well with each other and are closely associated with tumour prognosis. Most importantly, some subtypes of small lung adenocarcinomas with specific CT or pathologic features are curable. Therefore, all defining characteristics (CT, pathologic and prognostic) of this kind of tumour should be integrated to improve our understanding, provide guidelines for management and accurately assess its prognosis.

Computer-aided detection of small pulmonary nodules in chest radiographs: an observer study

RATIONALE AND OBJECTIVES

To evaluate the impact of computer-aided detection (CAD, IQQA-Chest; EDDA Technology, Princeton Junction, NJ) used as second reader on the detection of small pulmonary nodules in chest radiography (CXR).

MATERIALS AND METHODS

A total of 113 patients (mean age 62 years) with CT and CXR within 6 weeks were selected. Fifty-nine patients showed 101 pulmonary nodules (diameter 5-15mm); the remaining 54 patients served as negative controls. Six readers of varying experience individually evaluated the CXR without and with CAD as second reader in two separate reading sessions. The sensitivity per lesion, figure of merit (FOM), and mean false positive per image (mFP) were calculated. Institutional review board approval was waived.

RESULTS

With CAD, the sensitivity increased for inexperienced readers (39% vs. 45%, P < .05) and remained unchanged for experienced readers (50% vs. 51%). The mFP nonsignificantly increased for both inexperienced and experienced readers (0.27 vs. 0.34 and 0.16 vs. 0.21). The mean FOM did not significantly differ for readings without and with CAD irrespective of reader experience (0.71 vs. 0.71 and 0.84 vs. 0.87). All readers together dismissed 33% of true-positive CAD candidates. False-positive candidates by CAD provoked 40% of all false-positive marks made by the readers.

CONCLUSION

CAD improves the sensitivity of inexperienced readers for the detection of small nodules at the expense of loss of specificity. Overall performance by means of FOM was therefore not affected. To use CAD more beneficial, readers need to improve their ability to differentiate true from false-positive CAD candidates.

Usefulness of computerized method for lung nodule detection in digital chest radiographs using temporal subtraction images

RATIONALE AND OBJECTIVES

The aim of this study was to evaluate the usefulness of a novel computerized method for lung nodule detection on digital chest radiographs using temporal subtraction images.

MATERIALS AND METHODS

To significantly reduce the number of false-positive results while maintaining high sensitivity, temporal subtraction images, which can enhance interval changes on sequential chest radiographs, were used. Fifty-one cases with lung nodules <3 cm and 51 cases without lung nodules were selected for an observer performance test. Twelve radiologists participated in this observer performance test. The radiologists' performance was evaluated using receiver-operating characteristic analysis, on a continuous rating scale. To estimate the numbers of cases affected beneficially and those affected detrimentally using this computerized method, the computer output was assumed to have an effect on an observer's diagnosis when there was a difference in rating score of ≥30% between the first and second ratings.

RESULTS

The average area under the curve for all radiologists increased significantly from 0.849 to 0.950 with the computerized method (P < .001). The mean number of cases affected beneficially was significantly higher than that of cases affected detrimentally (8.92 vs 1.25, P < .001).

CONCLUSIONS

The novel computerized method using temporal subtraction images would be useful in detecting lung nodules on digital chest radiographs.

Biplane correlation imaging: a feasibility study based on phantom and human data

The objective of this study was to implement and evaluate the performance of a biplane correlation imaging (BCI) technique aimed to reduce the effect of anatomic noise and improve the detection of lung nodules in chest radiographs. Seventy-one low-dose posterior-anterior images were acquired from an anthropomorphic chest phantom with 0.28° angular separations over a range of ±10° along the vertical axis within an 11 s interval. Similar data were acquired from 19 human subjects with institutional review board approval and informed consent. The data were incorporated into a computer-aided detection (CAD) algorithm in which suspect lesions were identified by examining the geometrical correlation of the detected signals that remained relatively constant against variable anatomic backgrounds. The data were analyzed to determine the effect of angular separation, and the overall sensitivity and false-positives for lung nodule detection. The best performance was achieved for angular separations of the projection pairs greater than 5°. Within that range, the technique provided an order of magnitude decrease in the number of false-positive reports when compared with CAD analysis of single-view images. Overall, the technique yielded ~1.1 false-positive per patient with an average sensitivity of 75%. The results indicated that the incorporation of angular information can offer a reduction in the number of false-positives without a notable reduction in sensitivity. The findings suggest that the BCI technique has the potential for clinical implementation as a cost-effective technique to improve the detection of subtle lung nodules with lowered rate of false-positives.

Primary care radiography in the early diagnosis of lung cancer

Finding an abnormality on a plain chest radiograph is usually the first definite evidence of a lung cancer, so this investigation is currently pivotal in the diagnosis of the disease. Although the National Institute for Clinical Excellence (NICE) has produced guidance on when a chest radiograph should be done for putative lung cancer presentations, cancer will usually be only one of a number of possible diagnoses, so this is somewhat artificial. Neither is there any evidence that obtaining a chest radiograph for these features leads to an improved outcome. Another major concern is the poor public awareness of the symptoms for which a chest radiograph is recommended. This article discusses the role of the chest radiograph in the early diagnosis of lung cancer with particular emphasis on the limited value of a single negative result and on the potential implications of interventions to increase the number of chest radiographs done in primary care.

Improved detection of pulmonary nodules on energy-subtracted chest radiographs with a commercial computer-aided diagnosis software: comparison with human observers

OBJECTIVE

To retrospectively analyze the performance of a commercial computer-aided diagnosis (CAD) software in the detection of pulmonary nodules in original and energy-subtracted (ES) chest radiographs.

METHODS

Original and ES chest radiographs of 58 patients with 105 pulmonary nodules measuring 5-30 mm and images of 25 control subjects with no nodules were randomized. Five blinded readers evaluated firstly the original postero-anterior images alone and then together with the subtracted radiographs. In a second phase, original and ES images were analyzed by a commercial CAD program. CT was used as reference standard. CAD results were compared to the readers' findings. True-positive (TP) and false-positive (FP) findings with CAD on subtracted and non-subtracted images were compared.

RESULTS

Depending on the reader's experience, CAD detected between 11 and 21 nodules missed by readers. Human observers found three to 16 lesions missed by the CAD software. CAD used with ES images produced significantly fewer FPs than with non-subtracted images: 1.75 and 2.14 FPs per image, respectively (p = 0.029). The difference for the TP nodules was not significant (40 nodules on ES images and 34 lesions in non-subtracted radiographs, p = 0.142).

CONCLUSION

CAD can improve lesion detection both on energy subtracted and non-subtracted chest images, especially for less experienced readers. The CAD program marked less FPs on energy-subtracted images than on original chest radiographs.

Temporal subtraction chest radiography

Radiologist are commonly required to compare a sequence of two or more chest radiographs of a given patient obtained over a period of time, which may range from a few hours to many years. In such cases, the task is one of detecting interval change. In the case of patients who have had a previous chest radiograph, an opportunity exists to enhance selectively areas of interval change, including regions with new or altered pathology, by using the previous radiographs as a subtraction mask. With temporal subtraction, the previous image is superimposed and registered with the current image, using automated two-dimensional warping to compensate for any differences in positioning. A "difference image" is then created, by subtracting the previous from the current radiograph. In this temporal subtraction image, areas that are unchanged appear as uniform gray, while regions of new opacity, such as due to pneumonia or cancer, appear as prominent dark foci on a lighter background. By cancelling out the complex anatomical background, temporal subtraction can provide dramatically enhanced visibility of new areas of disease.

The Reference Image Database to Evaluate Response to therapy in lung cancer (RIDER) project: a resource for the development of change-analysis software

Critical to the clinical evaluation of effective novel therapies for lung cancer is the early and accurate determination of tumor response, which requires an understanding of the sources of uncertainty in tumor measurement and subsequent attempts to minimize their effects on the assessment of the therapeutic agent. The Reference Image Database to Evaluate Response (RIDER) project seeks to develop a consensus approach to the optimization and benchmarking of software tools for the assessment of tumor response to therapy and to provide a publicly available database of serial images acquired during lung cancer drug and radiation therapy trials. Images of phantoms and patient images acquired under situations in which tumor size or biology is known to be unchanged also will be provided. The RIDER project will create standardized methods for benchmarking software tools to reduce sources of uncertainty in vital clinical assessments such as whether a specific tumor is responding to therapy.

Evaluation of computer-aided diagnosis (CAD) software for the detection of lung nodules on multidetector row computed tomography (MDCT): JAFROC study for the improvement in radiologists' diagnostic accuracy

RATIONALE AND OBJECTIVES

The aim of this study was to evaluate the usefulness of computer-aided diagnosis (CAD) software for the detection of lung nodules on multidetector-row computed tomography (MDCT) in terms of improvement in radiologists' diagnostic accuracy in detecting lung nodules, using jackknife free-response receiver-operating characteristic (JAFROC) analysis.

MATERIALS AND METHODS

Twenty-one patients (6 without and 15 with lung nodules) were selected randomly from 120 consecutive thoracic computed tomographic examinations. The gold standard for the presence or absence of nodules in the observer study was determined by consensus of two radiologists. Six expert radiologists participated in a free-response receiver operating characteristic study for the detection of lung nodules on MDCT, in which cases were interpreted first without and then with the output of CAD software. Radiologists were asked to indicate the locations of lung nodule candidates on the monitor with their confidence ratings for the presence of lung nodules.

RESULTS

The performance of the CAD software indicated that the sensitivity in detecting lung nodules was 71.4%, with 0.95 false-positive results per case. When radiologists used the CAD software, the average sensitivity improved from 39.5% to 81.0%, with an increase in the average number of false-positive results from 0.14 to 0.89 per case. The average figure-of-merit values for the six radiologists were 0.390 without and 0.845 with the output of the CAD software, and there was a statistically significant difference (P < .0001) using the JAFROC analysis.

CONCLUSION

The CAD software for the detection of lung nodules on MDCT has the potential to assist radiologists by increasing their accuracy.

Improved detection of small lung cancers with dual-energy subtraction chest radiography

OBJECTIVE

The objective of our study was to retrospectively evaluate whether the use of dual-energy subtraction chest radiographs can improve radiologists' performance for the detection of small previously missed lung cancers.

MATERIALS AND METHODS

Dual-energy subtraction chest radiographs of 19 patients with previously missed nodular cancers, in which the radiology report did not mention a nodule that was visible in retrospect, were selected. Dual-energy subtraction radiographs of 19 patients with cancer and 16 patients without cancer were used for an observer study. Six radiologists indicated their confidence level regarding the presence of a lung cancer and, if they thought a cancer was present, also marked the most likely position for each lung, first using standard posteroanterior and lateral chest radiographs and then using both soft-tissue and bone dual-energy subtraction images along with standard radiographs. Receiver operating characteristic (ROC) curves were used to evaluate the observers' performance. The indicated locations of cancers and false-positives were also analyzed.

RESULTS

The average area under the ROC curve (A(z)) value for the six radiologists was improved from 0.718 to 0.816, a statistically significant amount (p = 0.004), and the average sensitivity (correct localizations) for 19 previously missed cancers was also significantly improved from 40% to 59% (p = 0.008) with the aid of dual-energy subtraction images. The average number of false-positive (incorrect) localizations on 70 lungs was 10 without and nine with dual-energy subtraction images (p = 0.785).

CONCLUSION

Dual-energy subtraction chest radiography has the potential to improve radiologists' performance for the detection of small missed lung cancers.

Lung cancers missed on chest radiographs: results obtained with a commercial computer-aided detection program

PURPOSE

To retrospectively determine the sensitivity of and number of false-positive marks made by a commercially available computer-aided detection (CAD) system for identifying lung cancers previously missed on chest radiographs by radiologists, with histopathologic results as the reference standard.

MATERIALS AND METHODS

Institutional review board approval was obtained for this HIPAA-compliant study; the requirement for informed patient consent was waived. A CAD nodule detection program was applied to 34 posteroanterior digital chest radiographs obtained in 34 patients (21 men, 13 women; mean age, 69 years). All 34 radiographs showed a nodular lung cancer that was apparent in retrospect but had not been mentioned in the report. Two radiologists identified these radiologist-missed cancers on the chest radiographs and graded them for visibility, location, subtlety (extremely subtle to extremely obvious on a 10-point scale), and actionability (actionable or not actionable according to whether the radiologists probably would have recommended follow-up if the nodule had been detected). The CAD results were analyzed to determine the numbers of cancers and false-positive nodules marked and to correlate the CAD results with the nodule grades for subtlety and actionability. The chi2 test or Fisher exact test for independence was used to compare CAD sensitivity between the very subtle (grade 1-3) and relatively obvious (grade > 3) cancers and between the actionable and not actionable cancers.

RESULTS

The CAD program had an overall sensitivity of 35% (12 of 34 cancers), identifying seven (30%) of 23 very subtle and five (45%) of 11 relatively obvious radiologist-missed cancers (P = .21) and detecting two (25%) of eight missed not actionable and ten (38%) of 26 missed actionable cancers (P = .33). The CAD program made an average of 5.9 false-positive marks per radiograph.

CONCLUSION

The described CAD system can mark a substantial proportion of visually subtle lung cancers that are likely to be missed by radiologists.

Multiprojection correlation imaging for improved detection of pulmonary nodules

OBJECTIVE

The purpose of this study was the development and preliminary evaluation of multiprojection correlation imaging with 3D computer-aided detection (CAD) on chest radiographs for cost- and dose-effective improvement of early detection of pulmonary nodules.

SUBJECTS AND METHODS

Digital chest radiographs of 10 configurations of a chest phantom and of seven human subjects were acquired in multiple angular projections with an acquisition time of 11 seconds (single breath-hold) and total exposure comparable with that of a posteroanterior chest radiograph. An initial 2D CAD algorithm with two difference-of-gaussians filters and multilevel thresholds was developed with an independent database of 44 single-view chest radiographs with confirmed lesions. This 2D CAD algorithm was used on each projection image to find likely suspect nodules. The CAD outputs were reconstructed in 3D, reinforcing signals associated with true nodules while simultaneously decreasing false-positive findings produced by overlapping anatomic features. The performance of correlation imaging was tested on two to 15 projection images.

RESULTS

Optimum performance of correlation imaging was attained when nine projection images were used. Compared with conventional, single-view CAD, correlation imaging decreased as much as 79% the frequency of false-positive findings in phantom cases at a sensitivity level of 65%. The corresponding reduction in false-positive findings in the cases of human subjects was 78%.

CONCLUSION

Although limited by a relatively simple CAD implementation and a small number of cases, the findings suggest that correlation imaging performs substantially better than single-view CAD and may greatly enhance identification of subtle solitary pulmonary nodules on chest radiographs.

Five-year lung cancer screening experience: CT appearance, growth rate, location, and histologic features of 61 lung cancers

PURPOSE

To retrospectively evaluate the computed tomography (CT)-determined size, morphology, location, morphologic change, and growth rate of incidence and prevalence lung cancers detected in high-risk individuals who underwent annual chest CT screening for 5 years and to evaluate the histologic features and stages of these cancers.

MATERIALS AND METHODS

The study was institutional review board approved and HIPAA compliant. Informed consent was waived. CT scans of 61 cancers (24 in men, 37 in women; age range, 53-79 years; mean, 65 years) were retrospectively reviewed for cancer size, morphology, and location. Forty-eight cancers were assessed for morphologic change and volume doubling time (VDT), which was calculated by using a modified Schwartz equation. Histologic sections were retrospectively reviewed.

RESULTS

Mean tumor size was 16.4 mm (range, 5.5-52.5 mm). Most common CT morphologic features were as follows: for bronchioloalveolar carcinoma (BAC) (n = 9), ground-glass attenuation (n = 6, 67%) and smooth (n = 3, 33%), irregular (n = 3, 33%), or spiculated (n = 3, 33%) margin; for non-BAC adenocarcinomas (n = 25), semisolid (n = 11, 44%) or solid (n = 12, 48%) attenuation and irregular margin (n = 14, 56%); for squamous cell carcinoma (n = 14), solid attenuation (n = 12, 86%) and irregular margin (n = 10, 71%); for small cell or mixed small and large cell neuroendocrine carcinoma (n = 7), solid attenuation (n = 6, 86%) and irregular margin (n = 5, 71%); for non-small cell carcinoma not otherwise specified (n = 5), solid attenuation (n = 4, 80%) and irregular margin (n = 3, 60%); and for large cell carcinoma (n = 1), solid attenuation and spiculated shape (n = 1, 100%). Attenuation most often (in 12 of 21 cases) increased. Margins most often (in 16 of 20 cases) became more irregular or spiculated. Mean VDT was 518 days. Thirteen of 48 cancers had a VDT longer than 400 days; 11 of these 13 cancers were in women.

CONCLUSION

Overdiagnosis, especially in women, may be a substantial concern in lung cancer screening.

Computer-aided diagnostic scheme for the detection of lung nodules on chest radiographs: localized search method based on anatomical classification

We developed an advanced computer-aided diagnostic (CAD) scheme for the detection of various types of lung nodules on chest radiographs intended for implementation in clinical situations. We used 924 digitized chest images (992 noncalcified nodules) which had a 500 x 500 matrix size with a 1024 gray scale. The images were divided randomly into two sets which were used for training and testing of the computerized scheme. In this scheme, the lung field was first segmented by use of a ribcage detection technique, and then a large search area (448 x 448 matrix size) within the chest image was automatically determined by taking into account the locations of a midline and a top edge of the segmented ribcage. In order to detect lung nodule candidates based on a localized search method, we divided the entire search area into 7 x 7 regions of interest (ROIs: 64 x 64 matrix size). In the next step, each ROI was classified anatomically into apical, peripheral, hilar, and diaphragm/heart regions by use of its image features. Identification of lung nodule candidates and extraction of image features were applied for each localized region (128 x 128 matrix size), each having its central part (64 x 64 matrix size) located at a position corresponding to a ROI that was classified anatomically in the previous step. Initial candidates were identified by use of the nodule-enhanced image obtained with the average radial-gradient filtering technique, in which the filter size was varied adaptively depending on the location and the anatomical classification of the ROI. We extracted 57 image features from the original and nodule-enhanced images based on geometric, gray-level, background structure, and edge-gradient features. In addition, 14 image features were obtained from the corresponding locations in the contralateral subtraction image. A total of 71 image features were employed for three sequential artificial neural networks (ANNs) in order to reduce the number of false-positive candidates. All parameters for ANNs, i.e., the number of iterations, slope of sigmoid functions, learning rate, and threshold values for removing the false positives, were determined automatically by use of a bootstrap technique with training cases. We employed four different combinations of training and test image data sets which was selected randomly from the 924 cases. By use of our localized search method based on anatomical classification, the average sensitivity was increased to 92.5% with 59.3 false positives per image at the level of initial detection for four different sets of test cases, whereas our previous technique achieved an 82.8% of sensitivity with 56.8 false positives per image. The computer performance in the final step obtained from four different data sets indicated that the average sensitivity in detecting lung nodules was 70.1% with 5.0 false positives per image for testing cases and 70.4% sensitivity with 4.2 false positives per image for training cases. The advanced CAD scheme involving the localized search method with anatomical classification provided improved detection of pulmonary nodules on chest radiographs for 924 lung nodule cases.

A fully automated method for lung nodule detection from postero-anterior chest radiographs

In the past decades, a great deal of research work has been devoted to the development of systems that could improve radiologists' accuracy in detecting lung nodules. Despite the great efforts, the problem is still open. In this paper, we present a fully automated system processing digital postero-anterior (PA) chest radiographs, that starts by producing an accurate segmentation of the lung field area. The segmented lung area includes even those parts of the lungs hidden behind the heart, the spine, and the diaphragm, which are usually excluded from the methods presented in the literature. This decision is motivated by the fact that lung nodules may be found also in these areas. The segmented area is processed with a simple multiscale method that enhances the visibility of the nodules, and an extraction scheme is then applied to select potential nodules. To reduce the high number of false positives extracted, cost-sensitive support vector machines (SVMs) are trained to recognize the true nodules. Different learning experiments were performed on two different data sets, created by means of feature selection, and employing Gaussian and polynomial SVMs trained with different parameters; the results are reported and compared. With the best SVM models, we obtain about 1.5 false positives per image (fp/image) when sensitivity is approximately equal to 0.71; this number increases to about 2.5 and 4 fp/image when sensitivity is = 0.78 and = 0.85, respectively. For the highest sensitivity (= 0.92 and 1.0), we get 7 or 8 fp/image.

Partnerships in oncology and radiology: the role of radiology in the detection, staging, and follow-up of lung cancer

In this review, I examine the multifaceted role of radiology in the diagnosis, staging, and management of lung cancer, highlighting new applications and modalities such as computer-aided detection of lung nodules and positron emission tomography/computed tomography for staging and monitoring response to therapy. Lung cancer screening is also discussed.

Computer-aided diagnosis for the detection and classification of lung cancers on chest radiographs ROC analysis of radiologists' performance

RATIONALE AND OBJECTIVES

The aim of the study is to investigate the effect of a computer-aided diagnostic (CAD) scheme on radiologist performance in the detection of lung cancers on chest radiographs.

MATERIALS AND METHODS

We combined two independent CAD schemes for the detection and classification of lung nodules into one new CAD scheme by use of a database of 150 chest images, including 108 cases with solitary pulmonary nodules and 42 cases without nodules. For the observer study, we selected 48 chest images, including 24 lung cancers, 12 benign nodules, and 12 cases without nodules, from the database to investigate radiologist performance in the detection of lung cancers. Nine radiologists participated in a receiver operating characteristic (ROC) study in which cases were interpreted first without and then with computer output, which indicated locations of possible lung nodules, together with a five-color scale illustrating the computer-estimated likelihood of malignancy of the detected nodules.

RESULTS

Performance of the CAD scheme indicated that sensitivity in detecting lung nodules was 80.6%, with 1.2 false-positive results per image, and sensitivity and specificity for classification of nodules by use of the same database for training and testing the CAD scheme were 87.7% and 66.7%, respectively. Average area under the ROC curve value for detection of lung cancers improved significantly (P = .008) from without (0.724) to with CAD (0.778).

CONCLUSION

This type of CAD scheme, which includes two functions, namely detection and classification, can improve radiologist accuracy in the diagnosis of lung cancer.

Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN)

When lung nodules overlap with ribs or clavicles in chest radiographs, it can be difficult for radiologists as well as computer-aided diagnostic (CAD) schemes to detect these nodules. In this paper, we developed an image-processing technique for suppressing the contrast of ribs and clavicles in chest radiographs by means of a multiresolution massive training artificial neural network (MTANN). An MTANN is a highly nonlinear filter that can be trained by use of input chest radiographs and the corresponding "teaching" images. We employed "bone" images obtained by use of a dual-energy subtraction technique as the teaching images. For effective suppression of ribs having various spatial frequencies, we developed a multiresolution MTANN consisting of multiresolution decomposition/composition techniques and three MTANNs for three different-resolution images. After training with input chest radiographs and the corresponding dual-energy bone images, the multiresolution MTANN was able to provide "bone-image-like" images which were similar to the teaching bone images. By subtracting the bone-image-like images from the corresponding chest radiographs, we were able to produce "soft-tissue-image-like" images where ribs and clavicles were substantially suppressed. We used a validation test database consisting of 118 chest radiographs with pulmonary nodules and an independent test database consisting of 136 digitized screen-film chest radiographs with 136 solitary pulmonary nodules collected from 14 medical institutions in this study. When our technique was applied to nontraining chest radiographs, ribs and clavicles in the chest radiographs were suppressed substantially, while the visibility of nodules and lung vessels was maintained. Thus, our image-processing technique for rib suppression by means of a multiresolution MTANN would be potentially useful for radiologists as well as for CAD schemes in detection of lung nodules on chest radiographs.

Bronchogenic carcinoma in solid organ transplant recipients

Malignancies are increased in some types of solid organ transplant patients receiving immunosuppressive therapy and are a significant contributor to patient morbidity and mortality. There may be a 100-fold increase in the incidence of de novo neoplasia in this population. The risk of lymphoproliferative malignancies is well appreciated. In contrast, the risk of solid tumors with their consequent morbidity and mortality is less well known, probably because of their common occurrence in the general population. Lung cancer is the most common cause of cancer death in the United States; therefore, lung cancer in patients undergoing organ transplantation would be expected to occur frequently on the basis of chance alone. However, the lung cancer risk is approximately 20 to 25 times that of the general population, with an incidence of 0.28% to 4.1% in patients after heart and lung transplant. Risk factors thought to contribute include cigarette smoking, advanced age at transplantation, and chronic immunosuppressive therapy. The role of transplantation (and consequent therapy) in the development of lung cancer in this high-risk population remains unclear. As in the nontransplant population, adequate screening techniques are lacking, making early diagnosis and treatment a challenge. Despite close follow-up and routine imaging with chest radiography and CT, lung cancers continue to be discovered incidentally and at advanced stages. Treatment is similar to that of patients who are nontransplanted with similar stage, histology, and performance status.

Detection of Small Pulmonary Nodules Using Direct Digital Radiography and Picture Archiving and Communication Systems

PURPOSE

To evaluate the detection of small pulmonary nodules, in the diameter range of 5.4 to 15 mm, using direct digital chest imaging and soft copy interpretation on picture archiving and communication systems.

MATERIALS AND METHODS

The results of clinical computed tomography (CT) scans of the thorax were retrospectively reviewed from our radiology information system and picture archiving and communication systems archives. Patients with CT studies containing between 1 and 6 nodules, who also had a digital chest examination within 1 month of the CT scan were selected. Thirty patients with suitable nodules and 30 without nodules were included and form the data base for this study. The nodules were between 5.4 and 15 mm in average diameter. Four separate observers independently viewed the frontal and lateral chest studies of the 60 patients. The presence or absence of nodules was determined. Data were analyzed with Kappa, McNemar and Fischer exact tests for agreement and differences between observers, nodule size, and nodule zone.

RESULTS

A total of 42 nodules between 5.4 and 15 mm were present. The overall detection rate for the 4 observers was 41.7%. For nodules between 5.4 and 8 mm the detection rate was 26.2%. Agreement between observer's detection was poor to moderate. Differences between observers for both nodule size and zone were not significant. Only 1 observer had a relationship between nodule detection and nodule size.

CONCLUSIONS

Observer detection of pulmonary nodules in the range of 5 to 15 mm using current digital radiography systems is not reliable in the confusing environment of the lung. Additional modification of these systems is required to increase nodule conspicuity.

Screening for Non-Small Cell Lung Cancer

Lung cancer is the leading cause of cancer mortality and is usually discovered at an advanced stage, when treatment is generally not effective. Many researchers have investigated the value of screening for lung cancer, which would theoretically allow earlier detection and more effective treatment. Unfortunately, no trials of screening strategies for lung cancer have shown a mortality benefit, and as a result, no major medical organization currently recommends screening. Research continues to seek proof of the benefit of screening as new techniques are developed, including low-dose spiral computed tomography (CT), autofluorescence bronchoscopy, and advanced techniques of sputum analysis. Although there are promising data on the sensitivity of these newer screening methods, especially low-dose CT, for detecting early lung cancer, none of the published trials are controlled, and they have not yet proven a decrease in mortality. There are ongoing randomized, controlled trials aiming to demonstrate a mortality benefit. Patients who are interested in being screened for lung cancer should be encouraged to participate in well-designed clinical trials whenever possible.

False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network

RATIONALE AND OBJECTIVE

We developed a technique that uses a multiple massive-training artificial neural network (multi-MTANN) to reduce the number of false-positive results in a computer-aided diagnostic (CAD) scheme for detecting nodules in chest radiographs.

MATERIALS AND METHODS

Our database consisted of 91 solitary pulmonary nodules, including 64 malignant nodules and 27 benign nodules, in 91 chest radiographs. With our current CAD scheme based on a difference-image technique and linear discriminant analysis, we achieved a sensitivity of 82.4%, with 4.5 false positives per image. We developed the multi-MTANN for further reduction of the false positive rate. An MTANN is a highly nonlinear filter that can be trained with input images and corresponding teaching images. To reduce the effects of background levels in chest radiographs, we applied a background-trend-correction technique, followed by contrast normalization, to the input images for the MTANN. For enhancement of nodules, the teaching image was designed to contain the distribution for a "likelihood of being a nodule." Six MTANNs in the multi-MTANN were trained by using typical nodules and six different types of non-nodules (false positives).

RESULTS

Use of the trained multi-MTANN eliminated 68.3% of false-positive findings with a reduction of one true-positive result. The false-positive rate of our original CAD scheme was improved from 4.5 to 1.4 false positives per image, at an overall sensitivity of 81.3%.

CONCLUSION

Use of a multi-MTANN substantially reduced the false-positive rate of our CAD scheme for lung nodule detection on chest radiographs, while maintaining a level of sensitivity.

Lung Cancer Screening: Simulations of Effects of Imperfect Detection on Temporal Dynamics

PURPOSE

To use a mathematic model to demonstrate effects of imperfect detection on temporal dynamics of radiologic lung cancer screening.

MATERIALS AND METHODS

Monte Carlo simulations of lung cancer screening programs were performed in subjects at high risk for developing cancer. The effects of detection probabilities, symptomatic presentation of tumors, tumor volume doubling time, and time between screenings were examined. Computed tomography (CT) and chest radiography models were used.

RESULTS

For imperfect detection probabilities, the percentage of subjects with cancers detected with repeated screenings decreased to a steady-state value. The transition period was the period during which screenings were performed and detection rates decreased. At steady-state repeat screening, the proportion of subjects with cancers diagnosed at screening or by means of symptomatic presentation was determined by the annual probability of developing cancer and not by the sensitivity of the screening modality. The sensitivity of the screening technique did affect detected cancer size, number of interval cancers, and total number of cancers observed. CT was used to detect more total cancers over the course of the screening program and cancers with a smaller average size; moreover, fewer interval cancers were observed with CT screening than with chest radiography screening.

CONCLUSION

Lung cancer screening with imperfect detection has a transition period between baseline screening and steady-state behavior of annual screenings. Advantages of CT screening include a decrease in the average cancer size at detection, a decrease in the number of observed interval cancers, and an increase in the total number of cancers observed. Steady-state behavior indicates that long-term trials of screening may not be necessary.

Estimating the effects of informal radiology resident teaching on radiologist productivity: what is the cost of teaching?

RATIONALE AND OBJECTIVES

One mission of an academic radiology department is to teach. The greatest teaching effort is directed at radiology residents. As clinical work demands increase, informal, non-revenue-generating, teaching may suffer. We sought to determine the economic consequences of teaching.

MATERIALS AND METHODS

With the use of a picture archiving and communications system, 6 radiology faculty members independently interpreted and dictated digitally acquired bone and chest radiographs for 1 hour alone and again 10-12 weeks later with a first-year resident. During the second session, the quality of teaching was graded by independent observers. The number of cases, relative value units (RVUs), and reimbursement for each session were calculated.

RESULTS

The difference in number of cases dictated working alone (mean, 44.7) and with a first-year resident (mean, 23.5) was significant (P = 0.007). The difference between RVUs generated by faculty alone (mean, 9.0) and with a resident (mean, 4.5) also was significant (P = 0.006), and the difference in dollars billed when working alone (mean, $1558.45) and with a resident (mean, $777.65) was significant (P = 0.007). As teaching quality increased, the number of cases interpreted, dollars billed, and RVUs trended lower.

CONCLUSION

Informal resident teaching significantly reduces clinical throughput, reducing examination volume, RVUs, and dollars billed by approximately half.

Why Do Physicians Who Treat Lung Cancer Get Sued?

BACKGROUND

Minimal information exists on why malpractice actions are filed against physicians who treat lung cancer.

OBJECTIVE

To review currently available data on lung cancer malpractice litigation to develop litigation-avoidance strategies.

DESIGN

A retrospective review of a publicly available database containing verdicts and settlements of malpractice cases. Data were then compared to the Physician Insurers Association of America (PIAA) Lung Cancer Study, which was published in 1992. The PIAA report is considered the best available data on malpractice and lung cancer.

RESULTS

There were 89 patients in the current study and 213 patients in the PIAA study. Physicians are most often sued by patients in their 50s (mean age, 58.9 years; range, 34 to 80 years [current study]; vs 55 years; range, 17 to 75 years [PIAA study]). Primary care physicians (60% cases in the current study vs 33% cases in the PIAA study) and radiologists (20% cases in the current study vs 55% cases in the PIAA study) were named as defendants in > 75% of suits. Failure to diagnosis lung cancer was the most common reason physicians were sued (80% case in the current study vs 23.3% cases in the PIAA series). Despite the similarity in litigation profiles, the mean award to plaintiffs, in constant dollars, increased from $172,271 in the PIAA study to $632,261 in the current study.

CONCLUSIONS

(1) Recommended strategies to avoid litigation depend on physician subspecialties. While primary care physicians would benefit most from setting up a chest radiograph tracking system, radiologists would benefit most from initiating a continuous quality improvement system to substantially decrease the misinterpretation rate of chest radiographs. (2) Over the past 12 years, there appears to have been a substantial increase in awards to patients with lung cancer who sue their physicians. However, this finding may be artificial because of differing study design. Further investigation on this subject is recommended.

Detection of lung cancer on radiographs: receiver operating characteristic analyses of radiologists', pulmonologists', and anesthesiologists' performance

PURPOSE

To compare and quantify, by means of receiver operating characteristic (ROC) and localization ROC analyses, the performance of radiologists, pulmonologists, and anesthesiologists (residents and staff) in the detection of missed lung cancer.

MATERIALS AND METHODS

The study was approved by the institutional review board, and informed consent was not required or obtained for review of radiographs. A set of 60 posteroanterior chest radiographs was presented to 36 observers: 12 radiologists, 12 pulmonologists, and 12 anesthesiologists. Each of these three observer categories included six residents and six staff. Thirty of the radiographs each depicted one lung cancer that was overlooked at prospective image interpretation; the other 30 were normal radiographs matched for age and smoking history. Observers were asked to rate their degree of suspicion concerning the presence of lung cancer by using a visual analog scale and to point out the zone of suspicion on a schematic of the lung. These data were used to generate combined ROC-localization ROC curves and to assess performance. Intraobserver consistency was evaluated by using intraclass correlation coefficients and weighted kappa statistics.

RESULTS

Areas under the ROC curves indicated better performance for radiologists and pulmonologists compared with anesthesiologists (P < .002) and for staff compared with residents (P < .022). Performance was lower for all categories of observers when localization ROC curves were used. Radiologists and staff pulmonologists showed a higher degree of confidence in the assessment of normality than did other categories of physicians. Intraobserver consistency was poor.

CONCLUSION

Experienced readers showed better ability to distinguish normality from abnormality. Combined ROC and localization ROC analyses gave a more reliable quantification of observer performance than did ROC analysis alone.

Screening for lung cancer: current status and future directions: Thomas A. Neff lecture

Lung cancer is the number one cancer killer in North America. Currently, screening for lung cancer is not recommended. Therefore, patients will not receive a diagnosis until they present with symptomatic disease, which is usually advanced stage disease. Previous trials of screening with chest roentgenograms and sputum cytology have failed to show a decrease in lung cancer mortality. Some reports of screening with low-dose spiral CT scans have detected lung cancers at a smaller size (average size, 1.5 cm) than those usually detected by chest radiographs (mean size, 3.0 cm). Spiral CT scanning has been shown to detect between 58% and 85% of non-small cell lung cancers (NSCLCs) while they are in stage IA, and this compares favorably to the current medical practice, in which only 15% are detected as localized disease (Surveillance, Epidemiology, and End Results study data). This article summarizes the spiral CT screening data, and reviews some of the data related to screening with sputum cytology, sputum methylation, and autofluorescence bronchoscopy. Last, there is a brief discussion of some promising future strategies, with emphasis and data from studies presented at this Aspen Lung Conference.

Population screening for lung cancer

The feasibility of diagnosing small stage 1 lung cancers using low-dose chest computed tomography in asymptomatic at-risk individuals has been demonstrated in multiple studies. However, it has yet to be proved that the introduction of a chest computed tomography screening programme would do more good than harm at an acceptable cost.

Improved Small Volume Lung Cancer Detection with Computer-Aided Detection: Database Characteristics and Imaging of Response to Breast Cancer Risk Reduction Strategies

Computer-aided detection (CAD) and diagnosis (CADx) of in vivo imaging studies are important tools based on bioinformatics. Currently, there are two diseases for which the United States Food and Drug Administration (FDA) has given premarket approval (PMA): the detection of signs consistent with lung cancer on chest radiographs and breast cancer on mammograms. There are systems for other diseases and other types of images under development; however, this process depends on the availability of an accurate database. The author helped in the development of the databases for such systems and management of the clinical trial that resulted in the FDA-PMA of the system that detects findings consistent with lung cancer. The characteristics of the database used will be described. Further, a woman's risk of developing breast cancer differs from those of other women. Risk can be high, average, or low. There are now pharmaceuticals that decrease the risk that women, as a group, will develop breast cancer and it has been suggested that dietary changes could have similar effects. The pharmaceutical agents, though, have some associated side effects, and it is clinically important to determine whether these agents have decreased an individual woman's risk of breast cancer. In vivo imaging biomarkers of risk and successful risk reduction are therefore sought, but the information on possible in vivo imaging biomarkers is less mature than activities in CAD. Bioinformatics will be an important contributor to this in vivo imaging biomarker development.

Improved detection of lung nodules on chest radiographs using a commercial computer-aided diagnosis system

OBJECTIVE

The aim of this study was to evaluate the usefulness of a new commercially available computer-aided diagnosis (CAD) system with an automated method of detecting nodules due to lung cancers on chest radiograph.

MATERIALS AND METHODS

For patients with cancer, 45 cases with solitary lung nodules up to 25 mm in diameter (nodule size range, 8-25 mm in diameter; mean, 18 mm; median, 20 mm) were used. For healthy patients, 45 cases were selected on the basis of confirmation on chest CT. All chest radiographs were obtained with a computed radiography system. The CAD output images were produced with a newly developed CAD system, which consisted of an image server including CAD software called EpiSight/XR. Eight radiologists (four board-certified radiologists and four radiology residents) participated in observer performance studies and interpreted both the original radiographs and CAD output images using a sequential testing method. The observers' performance was evaluated with receiver operating characteristic analysis.

RESULTS

The average area under the curve value increased significantly from 0.924 without to 0.986 with CAD output images. Individually, the use of CAD output images was more beneficial to radiology residents than to board-certified radiologists.

CONCLUSION

This CAD system for digital chest radiographs can assist radiologists and has the potential to improve the detection of lung nodules due to lung cancer.