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 (CADe) and computer-aided diagnosis (CADx) have been important areas of research in the last two decades. Because of the high prevalence of breast cancer and challenges in interpretation of mammograms, most of the early work on CAD (CADe or CADx) was devoted to detection and characterization of masses and microcalcifications on mammograms. However, in the last decade, numerous studies on the development of CAD techniques have been reported for other diseases and imaging modalities. Lung cancer is the leading cause of cancer death in both men and women. The interpretation of thoracic computed tomographic (CT) scans for lung nodules is a demanding task for radiologists, and the risks of false-negative detection and benign nodules being recommended for biopsy are high. There is a potential for improvement if CADe and CADx are available for lung nodules in CT scans. A different, but related area is the detection of pulmonary embolism (PE) in CT pulmonary angiography (CTPA). PE is a common, potentially fatal condition in all age groups associated with significant morbidity and mortality in untreated patients, and radiologists may benefit from CADe because of the complexity of the pulmonary vascular structures and the large number of vessels to be inspected for PE in each case. In this review, we will focus our discussion on CADe and CADx of lung nodules and CADe of PE on CT examinations.
Challenges in lung cancer detection on CT examinations
In the United States, it is estimated that there will be 160,390 deaths from lung cancer and that 213,380 new cases will be diagnosed in 2007 ( ). Lung cancer remains the leading cause of cancer death for men since the 1950s and for women since 1987. The overall prognosis of lung cancer is very poor. The 5-year survival rate is only about 16% for all stages combined ( ). However, if detected and resected at its earliest stage (stage I), the 5-year survival rate can reach 70% ( ).
Unfortunately, previous studies failed to show a significant reduction in mortality by screening with chest radiography (CXR) ( ) despite the improvement in stage distribution, resectability, survival, and fatality in lung cancer. Lung cancer screening was therefore not recommended in clinical practice. Interest in screening was revived when computed tomography was shown to have higher sensitivity in detecting small, early stage lung cancer than CXR. The Early Lung Cancer Action Project (ELCAP) investigated the usefulness of annual low dose CT screening for lung cancer in a high-risk population and found that low-dose computed tomography can detect four times more malignant lung nodules than CXR, and six times more stage I malignant nodules, which potentially are more curable ( ). More recently, the International ELCAP (I-ELCAP) study showed that the 10-year survival of patients with stage I lung cancer detected on CT screening and who underwent surgical resection within 1 month reached 92%, and concluded that CT screening can detect lung cancer that is curable ( ). However, another multicenter study found that, in comparison with the predictions from a model, there were a threefold increase in cancer detection and 10-fold increase in lung resection, but no decline in diagnoses of advanced lung cancer or mortality rate ( ). A 30-site randomized controlled study (National Lung Screening Trial), sponsored by the National Cancer Institute, has enrolled about 50,000 participants to compare the effect of screening using helical computed tomography or chest x-rays on the mortality rate of lung cancer patients. The results of the study will not be available until about 2010.
Although there is controversy over whether CT screening may reduce lung cancer mortality, there is consensus that computed tomography allows the detection of more and smaller lung nodules than CXR. In the National Emphysema Treatment Trial, 25.6% of the 446 emphysema patients were found to have noncalcified nodules ( ). In ELCAP, 23.3% of the patients were found to have noncalcified nodules by computed tomography, which represented a threefold increase in sensitivity than CXRs ( ). This increase in sensitivity comes at the price of an increased workload for radiologists. A major potential difficulty in using helical computed tomography for screening is the dramatic increase in the number of images that need to be interpreted for each case. Another potential difficulty is the additional resources that will be needed for clinical management of the expected screening detected nodules. Different criteria are being used by physicians to manage lung nodules in current clinical practice ( ). Many nodules are recommended to be followed up. However, the rate of the screening detected nodules being resected is still high at about 20–40% ( ). A study of 426 patients who underwent video-assisted thoracoscopic surgery ( ) indicated that 42.5% of these cases were benign. Keagy et al ( ) found that 40% of their patients with benign nodules were subjected to thoracotomy for presumed malignant disease. It is therefore important to establish, if possible, more reliable criteria to estimate the likelihood of malignancy of the lung nodules based on image information without resorting to invasive procedures, thereby reducing the potential patient morbidity and additional health care costs associated with lung cancer screening. 2-[ 18 F]fluoro-2-deoxy-D-glucose–enhanced positron emission tomographic scans ( ) have been found to provide high sensitivity and good specificity for differentiating nodules as malignant and benign, but the procedure will involve radioactivity, relatively high costs, and may not be available in many medical facilities. The I-ELCAP study ( ) demonstrated that, with workup protocols that mainly used repeated CT scans to estimate nodule growth, the negative biopsy rate could be as low as 8% in the patient cohort. However, short-term follow-up with repeated computed tomography will further increase radiologists’ workload.
Although computed tomography has a much higher sensitivity than CXR, missed cancers are not uncommon in CT interpretation ( ). The main causes for missed cancers include detection errors and characterization errors. Detection errors can be attributed to factors such as oversight or failure to detect the lesion among other structures. Characterization errors may be attributed to the difficulty in differentiating malignant lesions from benign nodules. The latter can also cause a radiologist to overestimate the likelihood of malignancy and recommend biopsy for benign lesions. Double reading may reduce missed diagnoses, but it doubles the demand on radiologists’ time. Some criteria have been suggested to estimate the likelihood of malignancy of solitary pulmonary nodules ( ). Computer-assisted classification of malignant and benign lung nodules has been attempted and promising results have been reported ( ). Gurney et al ( ) used Bayesian analysis and an artificial neural network ( ) to classify radiographic and clinical features and achieved a higher accuracy than subjective classification by radiologists. However, these computer classifiers used radiologist-identified image features, the extraction of which are both time consuming and subject to inter- and intra-observer variations. Subtle change in nodule volume, especially when the nodule is small, is difficult to discern visually on CT images.
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Computerized detection of lung nodules
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Table 1
Studies on the Development of CADe Systems for Detection of Lung Nodules in Thoracic Computed Tomography Examinations
References No. of Cases (No. of Exams) Section Thickness/Interval Nodule Sizes Total No. of Nodules Sensitivity FP Rate Giger et al, 1994 ( ) 8 10 mm 3–18 mm 2–21/case 94% 1.25/case Kanazawa et al, 1998 ( ) 450 (Screening in Japan) 10 mm > 4 mm? 230 90% not reported Armato et al, 2001 ( ) 43 7–10 mm
171 70% 1.5/section Ko et al, 2001 ( ) 8 (16 exams)
3 mm 295 91% 2.3/case Brown et al, 2001 ( ) 17 5–10 mm 5–30 mm 36 86% 11/case Lee et al, 2001 ( ) 20 10 mm
98 72%
Armato et al, 2002 ( ) 31 (38 LD exams) 10 mm
50 80%
Wormanns et al, 2002 ( ) 85 (88 LD exams) 5 mm 5–16 mm 68 38% 5.8/case Gurcan et al, 2002 ( ) 34 5 mm
63 84% 1.74/slice Armato et al, 2003 ( ) 38 5 mm
82
Suzuki et al, 2003 ( ) 63 (LD) 10 mm
71 80.3%
Brown et al, 2003 ( ) 15 0.5–1 mm
79
15/case (2 cm of thorax) Zhao et al, 2003 ( )
2–7 mm
Goo et al, 2003 ( ) 50 7–8 mm ≥5 mm 26 65% 8/case Paik et al, 2004 ( ) 8 Not reported ≥6 mm Not reported 90% 5.6/case Lee et al, 2004 ( ) 15 1–1.25 mm
309
28.8/case Arimura et al, 2004 ( ) 106 10 mm 6–26 mm 131 81% 0.28/section McCulloch et al, 2004 ( ) 50 (LD) 2.5 mm 5.0–17.1 mm
70% 8.3/case Awai et al, 2004 ( ) 82 7.5 mm
78 80%
Ge et al, 2005 ( ) 56 (82 exams) 1–2.5 mm
116 80% 0.34/section Bae et al, 2005 ( ) 20 1 mm 3–27 mm 164 95.1% 6.9/case Rubin et al, 2005 ( ) 20 1.25/0.6 mm
195
Lin et al, 2005 ( ) 29 10 mm 10–30 mm 393 89.3% 0.21/section Kim et al, 2005 ( ) 10
≥3 mm
Li et al, 2005 ( ) 38 (LD, missed cancers) 10 mm
87% 3/case Armato et al, 2005 ( ) 393 (LD) 10 mm 3–30 mm
Kim et al, 2005 ( ) 14 1–5 mm
29 GGO 89.7% 0.89/section Brown et al, 2005 ( ) 8 (LD) 1.25 mm
22 86.4% 2.64 FPs/case (40 sections/case, 5 cm of thorax) Marten et al, 2005 ( ) (ICAD) 20 0.75 mm/0.6 mm
135 76.3% 0.55/case Marten et al, 2005 ( ) (ICAD) 20
135 ⁎
Peldschus et al, 2005 ( ) (R2 system) 100 1.25–3 mm Not reported 160 Reference radiologists checked CADe marks only 1.25/case Roy et al, 2006 ( ) 38 5 mm
82 70% 0.28/section or 0.03/section Boroczky et al, 2006 ( ) 25 (38 scans) 1.3 mm >3.5 mm 52 100% (no loss of TP in FP classification) –56.4% (FP reduction) Sahiner et al, 2006 ( ) 27 1–2.5 mm
80%
Sahiner et al, 2007 ( ) 48 (30 positive, 18 negative) 1.5–3 mm
70 79% 4.9/case Wang et al, 2007 ( ) 12 3 mm 4–20 mm 47 100% 1.75/case Sahiner et al, 2007 ( ) 85 (52 positive, 33 negative) 1.5–3 mm
118 78% 5.5/case
LD, low-dose computed tomography; GGO, ground-glass opacity; R2, CADe system by R2 Technologies; ICAD, CADe system by Siemens Medical Solutions; CADe, computer-aided detection; TP, true positive; FP, false positive.
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Effect of CADe on radiologists’ detection of lung nodules
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Table 2
Observer Performance Studies for Evaluation of the Effects of CADe on Radiologists’ Detection of Lung Nodules in Thoracic Computed Tomography Examinations
References No. of Exams Section Thickness/Interval Nodule Sizes Total No. of Nodules No. of Observers Reading without CAD Reading with CAD_P_ Value Awai et al, 2004 ( ) 50 (36 pos, 14 neg) 7.5 mm
56
Marten et al, 2004 ( ) (ICAD) 18 0.75/0.6 mm
96 (89 solid, 2 mixed, 5 calcified) 4 rad, 2 read with CAD
Marten et al, 2005 ( ) (ICAD) 20
135 ⁎ 2
Li et al, 2005 ( ) 27 (17 pos, 10 neg) (LD) 10 mm
18 (6 GGO 10 mixed 1 solid)
Brown et al, 2005 ( ) 8 (6 pos, 2 neg) (LD) (5 cm of thorax) 1.25 mm
22
Rubin et al, 2005 ( ) (Simulation) 20 (19 pos, 1 neg) 1.25/0.6 mm
195 3
< .05 Das et al, 2006 ( ) (R2, NEV) 25 (23 pos, 2 neg)
116 3
Yuan et al, 2006 ( ) (Simulation) 150 (134 pos, 16 neg)
628 1
Predicted sens increase 21.2% — Sahiner et al, 2007 ( ) 48 (30 pos, 18 neg) 1.5–3 mm
70 4
.03 Sahiner et al, 2007 ( ) 85 (52 pos, 33 neg) 1.5–3 mm
118 6
LD, low-dose computed tomography; pos, positive; neg, negative; rad, radiologists; resid, residents; AFROC, alternative free response receiver-operating characteristic; JAFROC, jackknife FROC; FOM, figure of merit; R2, CADe system by R2 Technologies; NEV, CADe system by Siemens Medical Solutions; ICAD, CADe system by Siemens Medical Solutions.
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Computerized characterization of lung nodules
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Table 3
Studies on the Development of CADx Systems for Characterization of Malignant and Benign Lung Nodules on Thoracic Computed Tomography Examinations
References No. of Exams Section Thickness/Interval Nodule Sizes Total No. of Nodules Malignant Benign_A z_ Accuracy Sensitivity Specificity Henschke et al, 1997 ( ) Not reported 5 mm <30 mm 28 14 14 ∼0.79 (from graph) 89% (25/28) 100% 79% Kawata et al, 1998 ( ) 62 1 mm 6–25 mm 62 47 15 Not reported McNitt-Gray et al, 1999 ( ) 31 ≤3 mm 5–30 mm 31 17 14 Not reported 90.3% (28/31) 88.2% 92.9% McNitt-Gray et al, 1999 ( ) 32 ≤3 mm
32 19 13 0.992–1.0 90.6–100% 89.5–100% 92.3–100% Matsuki et al, 2002 ( ) 155 2 mm <30 mm 155 99 56 0.951 Lo et al, 2003 ( ) 48 Thin-section CT Not reported 48 24 24 0.89 Armato et al, 2003 ( ) 393 (LD) 10 mm
0.79 Aoyama et al, 2003 ( ) 415 (LD) 10 mm <30 mm 489 76 413 0.846 Kawata et al, 2004 ( ) 174 0.5 mm Not reported 174 98 76 Not reported Li et al, 2004 ( ) 228 1 mm 3–20 mm 244 61 183 0.937 Suzuki et al, 2005 ( ) 415 (LD) 10 mm <30 mm 489 76 413 0.882 100% 48% Shah et al, 2005 ( ) 81 ≤3 mm
81 48 33 0.92 Shah et al, 2005 ( ) 54 not reported
54 33 21 81% 91% 67% Shah et al,. 2005 ( ) 35 pre-, post-contrast enhanced ≤3 mm
35 19 16 0.69–0.92 Mori et al, 2005 ( ) 62 pre-, post-contrast enhanced 2 mm
62 35 27
Awai et al, 2006 ( ) 33 1–1.25 mm <30 mm 33 18 15 0.795 73% 72% 75% Way et al, 2006 ( ) 58 1.25–5 mm
96 44 52 0.83 Way et al, 2007 ( ) 152 1–7.5 mm 3–36 mm 256 124 132 0.86 Hadjiiski et al, 2007 ( ) 43 0.625–3 mm 2–30 mm 103 39 64 0.85
LD, low-dose computed tomography.
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Effect of CADx on radiologists’ characterization of lung nodules
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Table 4
Observer Performance Studies for Evaluation of the Effects of CADx for Characterization of Malignant and Benign Lung Nodules in Thoracic Computed Tomography Examinations
References No. of Exams Section Thickness/Interval Nodule Sizes Total No. of Nodules Malignant Benign_A z_ CAD System No. of Observers_A z_ without CAD_A z_ with CAD_P_ Value Matsuki et al, 2002 ( ) 50 2 mm <30 mm 50 25 25 0.951
56 1 mm 6–20 mm 56 28 28 0.831
0.785 0.853 .016 Shah et al, 2005 ( ) 28 Not reported
28 15 13
0.75 0.81 .02 Awai et al, 2006 ( ) 33 1–1.25 mm <30 mm 33 18 15 0.795
Way et al, 2007 ( ) 152 1–7.5 mm 3–36 mm 256 124 132 0.86 6 thoracic rad 0.82 0.84 < .01
CADx, computer-aided diagnosis; Rad, radiologists; resid, residents.
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Challenges in pulmonary embolism detection on CTPA examinations
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Computerized detection of pulmonary embolism
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Table 5
Studies on the Development of CADe Systems for Detection of Pulmonary Embolism in Computed Tomography Examinations
References No. of Cases No. of Positive Cases Section Thickness Presence of Artifacts and Lung Disease PE Location Total No. of PEi Sensitivity FP Rate Masutani et al, 2002 ( ) 19 11 1.5 mm No Not reported 21 (>10 mm 3 ) 85% 2.6/case Das et al, 2003 ( ) (R2) 33 33 0.75–1.25 mm Not reported Segmental and subseg 306 (186 seg, 120 subseg)
4/case Zhou et al, 2005 ( ) 14 14 1.25 mm 8 cases with extensive lung disease Proximal to subseg and subseg 163 (94 prox-subseg, 69 subseg)
14.4/case Digumarthy et al, 2006 ( ) (R2) 39 33 Not reported No Arteries ≥4 mm 270 92% 2.8/case Jeudy et al, 2006 ( ) (R2) 22 22 Not reported Not reported Segmental and subseg 251 (188 seg, 63 subseg)
1.8/case Das et al, 2006 ( ) (Siemens) 45 29 1 mm Not reported
213 82% median 3/case Zhou et al, 2007 ( ) 43 43 1.25 mm Yes Proximal to subseg, and subseg 435 (263 prox-subseg, 172 subseg)
24/case Schoepf et al, 2007 ( ) (R2) 36 23 1.25 mm 21 cases with lung disease Segmental and subseg
4.8/case Maizlin et al, 2007 ( ) (R2) 104 15 1.25 mm Lung disease not reported, severe motion cases excluded Central, segmental, subseg 45 (32 central, seg, 13 subseg)
0.93/case Buhmann et al, 2007 ( ) (Siemens) 40 18 1 mm 5 with motion artifacts, lung disease not reported Central and peripheral 212 (65 central, 147 peripheral)
3.85/case Engelke et al, 2007 ( ) (Siemens) 56 56 0.6 mm Nonanalyzable arteries excluded Mediastinal, lobar, segmental, subsegmental 1116 ⁎ (72 mediastinal, 133 lobar, 465 seg, 455 subseg)
4.1/case
CADe, computer-aided detection; PE, pulmonary embolism; PEi, pulmonary emboli; FP, false positive; seg, segmental arteries; subseg, subsegmental arteries; prox, proximal; R2, CADe system by R2 Technologies; Siemens, Siemens CADe system.
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Discussion
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