Efficacy of computer-aided detection as a second reader for 6-9-mm lesions at CT colonography: multicenter prospective trial

AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer-aided diagnosis in medical imaging

Rapid advances in artificial intelligence (AI) and machine learning, and specifically in deep learning (DL) techniques, have enabled broad application of these methods in health care. The promise of the DL approach has spurred further interest in computer-aided diagnosis (CAD) development and applications using both "traditional" machine learning methods and newer DL-based methods. We use the term CAD-AI to refer to this expanded clinical decision support environment that uses traditional and DL-based AI methods. Numerous studies have been published to date on the development of machine learning tools for computer-aided, or AI-assisted, clinical tasks. However, most of these machine learning models are not ready for clinical deployment. It is of paramount importance to ensure that a clinical decision support tool undergoes proper training and rigorous validation of its generalizability and robustness before adoption for patient care in the clinic. To address these important issues, the American Association of Physicists in Medicine (AAPM) Computer-Aided Image Analysis Subcommittee (CADSC) is charged, in part, to develop recommendations on practices and standards for the development and performance assessment of computer-aided decision support systems. The committee has previously published two opinion papers on the evaluation of CAD systems and issues associated with user training and quality assurance of these systems in the clinic. With machine learning techniques continuing to evolve and CAD applications expanding to new stages of the patient care process, the current task group report considers the broader issues common to the development of most, if not all, CAD-AI applications and their translation from the bench to the clinic. The goal is to bring attention to the proper training and validation of machine learning algorithms that may improve their generalizability and reliability and accelerate the adoption of CAD-AI systems for clinical decision support.

Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers

Recently, Computer Aided Diagnosis (CAD) systems have been proposed to help radiologists in detecting and characterizing Prostate Cancer (PCa). However, few studies evaluated the performances of these systems in a clinical setting, especially when used by non-experienced readers. The main aim of this study is to assess the diagnostic performance of non-experienced readers when reporting assisted by the likelihood map generated by a CAD system, and to compare the results with the unassisted interpretation. Three resident radiologists were asked to review multiparametric-MRI of patients with and without PCa, both unassisted and assisted by a CAD system. In both reading sessions, residents recorded all positive cases, and sensitivity, specificity, negative and positive predictive values were computed and compared. The dataset comprised 90 patients (45 with at least one clinically significant biopsy-confirmed PCa). Sensitivity significantly increased in the CAD assisted mode for patients with at least one clinically significant lesion (GS > 6) (68.7% vs. 78.1%, p = 0.018). Overall specificity was not statistically different between unassisted and assisted sessions (94.8% vs. 89.6, p = 0.072). The use of the CAD system significantly increases the per-patient sensitivity of inexperienced readers in the detection of clinically significant PCa, without negatively affecting specificity, while significantly reducing overall reporting time.

Development and Validation of a Deep Learning-based Automatic Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs

Background

Detection of active pulmonary tuberculosis on chest radiographs (CRs) is critical for the diagnosis and screening of tuberculosis. An automated system may help streamline the tuberculosis screening process and improve diagnostic performance.

Methods

We developed a deep learning–based automatic detection (DLAD) algorithm using 54c221 normal CRs and 6768 CRs with active pulmonary tuberculosis that were labeled and annotated by 13 board-certified radiologists. The performance of DLAD was validated using 6 external multicenter, multinational datasets. To compare the performances of DLAD with physicians, an observer performance test was conducted by 15 physicians including nonradiology physicians, board-certified radiologists, and thoracic radiologists. Image-wise classification and lesion-wise localization performances were measured using area under the receiver operating characteristic (ROC) curves and area under the alternative free-response ROC curves, respectively. Sensitivities and specificities of DLAD were calculated using 2 cutoffs (high sensitivity [98%] and high specificity [98%]) obtained through in-house validation.

Results

DLAD demonstrated classification performance of 0.977–1.000 and localization performance of 0.973–1.000. Sensitivities and specificities for classification were 94.3%–100% and 91.1%–100% using the high-sensitivity cutoff and 84.1%–99.0% and 99.1%–100% using the high-specificity cutoff. DLAD showed significantly higher performance in both classification (0.993 vs 0.746–0.971) and localization (0.993 vs 0.664–0.925) compared to all groups of physicians.

Conclusions

Our DLAD demonstrated excellent and consistent performance in the detection of active pulmonary tuberculosis on CR, outperforming physicians, including thoracic radiologists.

Influence of Monoenergetic Images at Different Energy Levels in Dual-Energy Spectral CT on the Accuracy of Computer-Aided Detection for Pulmonary Embolism

PURPOSE

To investigate the influence of monoenergetic images of different energy levels in spectral computed tomography (CT) on the accuracy of computer aided detection (CAD) for pulmonary embolism (PE).

MATERIALS AND METHODS

CT images of 20 PE patients who underwent spectral CT pulmonary angiography were retrospectively analyzed. Nine sets of monochromatic images from 40 to 80 keV at 5 keV interval were reconstructed and then independently analyzed for detecting PE using a commercially available CAD software. Two experienced radiologists reviewed all images and recorded the number of emboli manually, which was used as the reference standard. The CAD findings for the number of PE at different energies were compared with the reference standard to determine the number of true positives and false positives with CAD and to calculate the sensitivity and false positive rate at different energies.

RESULT

There were 120 true emboli. The total numbers of CAD-detected PE at 40-80 keV were 48, 67, 63, 87, 106, 115, 138, 157, and 226. Images at low energies had low sensitivities and low false positive rates; images at high energies had high sensitivities and high false positive rates. At 60 keV and 65 keV, CAD achieved sensitivity at 81.67% and 84.17%, respectively and false positive rate at 7.55% and 12.17%, respectively to provide the optimum combination of high sensitivity and low false positive rate.

CONCLUSION

Monochromatic images of different energies in dual-energy spectral CT affect the accuracy of CAD for PE. The combination of CAD with images at 60-65 keV provides the optimum combination of high sensitivity and low false positive rate in detecting PE.

Computer-based self-training for CT colonography with and without CAD

OBJECTIVES

To determine whether (1) computer-based self-training for CT colonography (CTC) improves interpretation performance of novice readers; (2) computer-aided detection (CAD) use during training affects learning.

METHODS

Institutional review board approval and patients' informed consent were obtained for all cases included in this study. Twenty readers (17 radiology residents, 3 radiologists) with no experience in CTC interpretation were recruited in three centres. After an introductory course, readers performed a baseline assessment test (37 cases) using CAD as second reader. Then they were randomized (1:1) to perform either a computer-based self-training (150 cases verified at colonoscopy) with CAD as second reader or the same training without CAD. The same assessment test was repeated after completion of the training programs. Main outcome was per lesion sensitivity (≥ 6 mm). A generalized estimating equation model was applied to evaluate readers' performance and the impact of CAD use during training.

RESULTS

After training, there was a significant improvement in average per lesion sensitivity in the unassisted phase, from 74% (356/480) to 83% (396/480) (p < 0.001), and in the CAD-assisted phase, from 83% (399/480) to 87% (417/480) (p = 0.021), but not in average per patient sensitivity, from 93% (390/420) to 94% (395/420) (p = 0.41), and specificity, from 81% (260/320) to 86% (276/320) (p = 0.15). No significant effect of CAD use during training was observed on per patient sensitivity and specificity, nor on per lesion sensitivity.

CONCLUSIONS

A computer-based self-training program for CTC improves readers' per lesion sensitivity. CAD as second reader does not have a significant impact on learning if used during training.

KEY POINTS

• Computer-based self-training for CT colonography improves per lesion sensitivity of novice readers. • Self-training program does not increase per patient specificity of novice readers. • CAD used during training does not have significant impact on learning.

Novel Developments in Endoscopic Mucosal Imaging

Endoscopic techniques such as high-definition and optical-chromoendoscopy have had enormous impact on endoscopy practice. Since these techniques allow assessment of most subtle morphological mucosal abnormalities, further improvements in endoscopic practice lay in increasing the detection efficacy of endoscopists. Several new developments could assist in this. First, web based training tools could improve the skills of the endoscopist for enhancing the detection and classification of lesions. Secondly, incorporation of computer aided detection will be the next step to raise endoscopic quality of the captured data. These systems will aid the endoscopist in interpreting the increasing amount of visual information in endoscopic images providing real-time objective second reading. In addition, developments in the field of molecular imaging open opportunities to add functional imaging data, visualizing biological parameters, of the gastrointestinal tract to white-light morphology imaging. For the successful implementation of abovementioned techniques, a true multi-disciplinary approach is of vital importance.

Post-imaging colorectal cancer or interval cancer rates after CT colonography: a systematic review and meta-analysis

BACKGROUND

CT colonography is highly sensitive for colorectal cancer, but interval or post-imaging colorectal cancer rates (diagnosis of cancer after initial negative CT colonography) are unknown, as are their underlying causes. We did a systematic review and meta-analysis of post-CT colonography and post-imaging colorectal cancer rates and causes to address this gap in understanding.

METHODS

We systematically searched MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials. We included randomised, cohort, cross-sectional, or case-control studies published between Jan 1, 1994, and Feb 28, 2017, using CT colonography done according to international consensus standards with the aim of detecting cancer or polyps, and reporting post-imaging colorectal cancer rates or sufficient data to allow their calculation. We excluded studies in which all CT colonographies were done because of incomplete colonoscopy or if CT colonography was done with knowledge of colonoscopy findings. We contacted authors of component studies for additional data where necessary for retrospective CT colonography image review and causes for each post-imaging colorectal cancer. Two independent reviewers extracted data from the study reports. Our primary outcome was prevalence of post-imaging colorectal cancer 36 months after CT colonography. We used random-effects meta-analysis to estimate pooled post-imaging colorectal cancer rates, expressed using the total number of cancers and total number of CT colonographies as denominators, and per 1000 person-years. This study is registered with PROSPERO, number CRD42016042437.

FINDINGS

2977 articles were screened and 12 studies were eligible for analysis. These studies reported data for 19 867 patients (aged 18-96 years; of 11 590 with sex data available, 6532 [56%] were female) between March, 2002, and May, 2015. At a mean of 34 months' follow-up (range 3-128·4 months), CT colonography detected 643 colorectal cancers. 29 post-imaging colorectal cancers were subsequently diagnosed. The pooled post-imaging colorectal cancer rate was 4·42 (95% CI 3·03-6·42) per 100 cancers detected, corresponding to 1·61 (1·11-2·33) post-imaging colorectal cancers per 1000 CT colonographies or 0·64 (0·44-0·92) post-imaging colorectal cancers per 1000 person-years. Heterogeneity was low (I²=0%). 17 (61%) of 28 post-imaging colorectal cancers were attributable to perceptual error and were visible in retrospect.

INTERPRETATION

CT colonography does not lead to an excess of post-test cancers relative to colonoscopy within 3-5 years, and the low 5-year post-imaging colorectal cancer rate confirms that the recommended screening interval of 5 years is safe. Since most post-imaging colorectal cancers arise from perceptual errors, radiologist training and quality assurance could help to reduce post-imaging colorectal cancer rates.

FUNDING

St Mark's Hospital Foundation and the UK National Institute for Health Research via the UCL/UCLH Biomedical Research Centre.

Multiparametric magnetic resonance imaging of the prostate with computer-aided detection: experienced observer performance study

OBJECTIVES

To compare the performance of experienced readers in detecting prostate cancer (PCa) using likelihood maps generated by a CAD system with that of unassisted interpretation of multiparametric magnetic resonance imaging (mp-MRI).

METHODS

Three experienced radiologists reviewed mp-MRI prostate cases twice. First, readers observed CAD marks on a likelihood map and classified as positive those suspicious for cancer. After 6 weeks, radiologists interpreted mp-MRI examinations unassisted, using their favourite protocol. Sensitivity, specificity, reading time and interobserver variability were compared for the two reading paradigms.

RESULTS

The dataset comprised 89 subjects of whom 35 with at least one significant PCa. Sensitivity was 80.9% (95% CI 72.1-88.0%) and 87.6% (95% CI 79.8-93.2; p = 0.105) for unassisted and CAD paradigm respectively. Sensitivity was higher with CAD for lesions with GS > 6 (91.3% vs 81.2%; p = 0.046) or diameter ≥10 mm (95.0% vs 80.0%; p = 0.006). Specificity was not affected by CAD. The average reading time with CAD was significantly lower (220 s vs 60 s; p < 0.001).

CONCLUSIONS

Experienced readers using likelihood maps generated by a CAD scheme can detect more patients with ≥10 mm PCa lesions than unassisted MRI interpretation; overall reporting time is shorter. To gain more insight into CAD-human interaction, different reading paradigms should be investigated.

KEY POINTS

• With CAD, sensitivity increases in patients with prostate tumours ≥10 mm and/or GS > 6. • CAD significantly reduces reporting time of multiparametric MRI. • When using CAD, a marginal increase of inter-reader agreement was observed.

Computer-aided detection of retained surgical needles from postoperative radiographs

PURPOSE

Foreign objects, such as surgical sponges, needles, sutures, and other surgical instruments, retained in the patient's body can have dire consequences in terms of patient mortality as well as legal and financial penalties. We propose computer-aided detection (CAD) on postoperative radiographs as a potential solution to reduce the chance of retained foreign objects (RFOs) after surgery, thus alleviating one of the major concerns for patient safety in the operation room. A CAD system can function as a second pair of eyes or a prescreener for the surgeon and radiologist, depending on the CAD system design and the workflow. In this work, we focus on the detection of surgical needles on postoperative radiographs. As needles are frequently observed RFOs, a CAD system that can offer high sensitivity and specificity toward detecting surgical needles will be useful.

METHODS

Our CAD system incorporates techniques such as image segmentation, image enhancement, feature analysis, and curve fitting to detect surgical needles on radiographs. A dataset consisting of 108 cadaver images with a total of 116 needles and 100 cadaver "normal" images without needles was acquired with a portable digital x-ray system. A reference standard was obtained by marking the needle locations using an in-house developed graphical user interface. The 108 cadaver images with the needles were partitioned into a training set containing 53 cadaver images with 59 needles and a test set containing 55 cadaver images with 57 needles. All of the 100 cadaver normal images were reserved as a part of the test set and used to estimate the false-positive detection rate. Two operating points were chosen from the CAD system such that it can be operated in two modes, one with higher specificity (mode I) and the other with higher sensitivity (mode II).

RESULTS

For the training set, the CAD system with the rule-based classifier achieved a sensitivity of 74.6% with 0.15 false positives per image (FPs/image) in mode I and a sensitivity of 89.8% with 0.36 FPs/image in mode II. For the test set, the CAD system achieved a sensitivity of 77.2% with 0.26 FPs/image in mode I and a sensitivity of 84.2% with 0.6 FPs/image in mode II. For comparison, the CAD system with the neural network classifier achieved a sensitivity of 74.6% with 0.08 FPs/image in mode I and a sensitivity of 88.1% with 0.28 FPs/image in mode II for the training set, and a sensitivity of 75.4% with 0.23 FPs/image in mode I and a sensitivity of 86.0% with 0.57 FPs/image in mode II for the test set.

CONCLUSION

A novel CAD system has been developed for automated detection of needles inadvertently left behind in a patient's body from postsurgery radiographs. The pilot system offers reasonable performance in both the high sensitivity and high specificity modes. This preliminary study shows the promise of CAD as a low-cost and efficient aid for reducing retained surgical needles in patients.

Accuracy of CT Colonography for Detection of Polypoid and Nonpolypoid Neoplasia by Gastroenterologists and Radiologists: A Nationwide Multicenter Study in Japan

OBJECTIVES

The objective of this study was to assess prospectively the diagnostic accuracy of computer-assisted computed tomographic colonography (CTC) in the detection of polypoid (pedunculated or sessile) and nonpolypoid neoplasms and compare the accuracy between gastroenterologists and radiologists.

METHODS

This nationwide multicenter prospective controlled trial recruited 1,257 participants with average or high risk of colorectal cancer at 14 Japanese institutions. Participants had CTC and colonoscopy on the same day. CTC images were interpreted independently by trained gastroenterologists and radiologists. The main outcome was the accuracy of CTC in the detection of neoplasms ≥6 mm in diameter, with colonoscopy results as the reference standard. Detection sensitivities of polypoid vs. nonpolypoid lesions were also evaluated.

RESULTS

Of the 1,257 participants, 1,177 were included in the final analysis: 42 (3.6%) were at average risk of colorectal cancer, 456 (38.7%) were at elevated risk, and 679 (57.7%) had recent positive immunochemical fecal occult blood tests. The overall per-participant sensitivity, specificity, and positive and negative predictive values for neoplasms ≥6 mm in diameter were 0.90, 0.93, 0.83, and 0.96, respectively, among gastroenterologists and 0.86, 0.90, 0.76, and 0.95 among radiologists (P<0.05 for gastroenterologists vs. radiologists). The sensitivity and specificity for neoplasms ≥10 mm in diameter were 0.93 and 0.99 among gastroenterologists and 0.91 and 0.98 among radiologists (not significant for gastroenterologists vs. radiologists). The CTC interpretation time by radiologists was shorter than that by gastroenterologists (9.97 vs. 15.8 min, P<0.05). Sensitivities for pedunculated and sessile lesions exceeded those for flat elevated lesions ≥10 mm in diameter in both groups (gastroenterologists 0.95, 0.92, and 0.68; radiologists: 0.94, 0.87, and 0.61; P<0.05 for polypoid vs. nonpolypoid), although not significant (P>0.05) for gastroenterologists vs. radiologists.

CONCLUSIONS

CTC interpretation by gastroenterologists and radiologists was accurate for detection of polypoid neoplasms, but less so for nonpolypoid neoplasms. Gastroenterologists had a higher accuracy in the detection of neoplasms ≥6 mm than did radiologists, although their interpretation time was longer than that of radiologists.

CT colonography for population screening of colorectal cancer: hints from European trials

CT colonography (CTC) is a minimally invasive radiological investigation of the colon. Robust evidence indicates that CTC is safe, well tolerated and highly accurate for the detection of colorectal cancer (CRC) and large polyps, which are the targets of screening. Randomized controlled trials were carried out in Europe to evaluate CTC as the primary test for population screening of CRC in comparison with faecal immunochemical test (FIT), sigmoidoscopy and colonoscopy. Main outcomes were participation rate and detection rate. Participation rate for screening CTC was in the range of 25-34%, whereas the detection rate of CTC for CRC and advanced adenoma was in the range of 5.1-6.1%. Participation for CTC screening was lower than that for FIT, similar to that for sigmoidoscopy and higher than that for colonoscopy. The detection rate of CTC was higher than that of one FIT round, similar to that of sigmoidoscopy and lower than that of colonoscopy. However, owing to the higher participation rate in CTC screening with respect to colonoscopy screening, the detection rates per invitee of CTC and colonoscopy would be comparable. These results justify consideration of CTC in organized screening programmes for CRC. However, assessment of other factors such as polyp size threshold for colonoscopy referral, management of extracolonic findings and, most importantly, the forthcoming results of cost-effectiveness analyses are crucial to define the role of CTC in primary screening.

CT Colonography: an update on current and future indications

Computed tomographic colonography (CTC) is a minimally invasive, patient-friendly, safe and robust colonic imaging modality. The technique is standardized and consolidated evidence from the literature shows that the diagnostic performances for the detection of colorectal cancer and large polyps are similar to colonoscopy (CS) and largely superior to alternative radiological exams, like barium enema. A clear understanding of the exact role of CTC will be beneficial to maximize the benefits and minimize the potential sources of frustration or disappointment for both referring clinicians and patients. Incomplete, failed, or unfeasible CS; investigation of elderly, and frail patients and assessment of diverticular disease are major indications supported by evidence-based data and agreed by the endoscopists. The use of CTC for symptomatic patients, colorectal cancer screening and colonic surveillance is still under debate and, thus, recommended only if CS is unfeasible or refused by patients.

Computed tomography colonography for the practicing radiologist: A review of current recommendations on methodology and clinical indications

Colorectal cancer (CRC) represents one of the most relevant causes of morbidity and mortality in Western societies. CRC screening is actually based on faecal occult blood testing, and optical colonoscopy still remains the gold standard screening test for cancer detection. However, computed tomography colonography (CT colonography) constitutes a reliable, minimally-invasive method to rapidly and effectively evaluate the entire colon for clinically relevant lesions. Furthermore, even if the benefits of its employment in CRC mass screening have not fully established yet, CT colonography may represent a reasonable alternative screening test in patients who cannot undergo or refuse colonoscopy. Therefore, the purpose of our review is to illustrate the most updated recommendations on methodology and the current clinical indications of CT colonography, according to the data of the existing relevant literature.

Imaging-based screening: maximizing benefits and minimizing harms

Advanced imaging technologies play a central role in screening asymptomatic patients. However, the balance between imaging-based screening's potential benefits versus risks is sometimes unclear. Radiologists will have to address ongoing concerns, including high false-positive rates, incidental findings outside the organ of interest, overdiagnosis, and potential risks from radiation exposure. In this article, we provide a brief overview of these recurring controversies and suggest the following as areas that radiologists should focus on in order to tip the balance toward more benefits and less harms for patients undergoing imaging-based screening: interpretive variability, abnormal finding thresholds, and personalized, risk-based screening.

CT colonography with computer-aided detection: recognizing the causes of false-positive reader results

Computed tomography (CT) colonography is a screening modality used to detect colonic polyps before they progress to colorectal cancer. Computer-aided detection (CAD) is designed to decrease errors of detection by finding and displaying polyp candidates for evaluation by the reader. CT colonography CAD false-positive results are common and have numerous causes. The relative frequency of CAD false-positive results and their effect on reader performance on the basis of a 19-reader, 100-case trial shows that the vast majority of CAD false-positive results were dismissed by readers. Many CAD false-positive results are easily disregarded, including those that result from coarse mucosa, reconstruction, peristalsis, motion, streak artifacts, diverticulum, rectal tubes, and lipomas. CAD false-positive results caused by haustral folds, extracolonic candidates, diminutive lesions (<6 mm), anal papillae, internal hemorrhoids, varices, extrinsic compression, and flexural pseudotumors are almost always recognized and disregarded. The ileocecal valve and tagged stool are common sources of CAD false-positive results associated with reader false-positive results. Nondismissable CAD soft-tissue polyp candidates larger than 6 mm are another common cause of reader false-positive results that may lead to further evaluation with follow-up CT colonography or optical colonoscopy. Strategies for correctly evaluating CAD polyp candidates are important to avoid pitfalls from common sources of CAD false-positive results.

Assessment of the Incremental Benefit of Computer-Aided Detection (CAD) for Interpretation of CT Colonography by Experienced and Inexperienced Readers

OBJECTIVES

To quantify the incremental benefit of computer-assisted-detection (CAD) for polyps, for inexperienced readers versus experienced readers of CT colonography.

METHODS

10 inexperienced and 16 experienced radiologists interpreted 102 colonography studies unassisted and with CAD utilised in a concurrent paradigm. They indicated any polyps detected on a study sheet. Readers' interpretations were compared against a ground-truth reference standard: 46 studies were normal and 56 had at least one polyp (132 polyps in total). The primary study outcome was the difference in CAD net benefit (a combination of change in sensitivity and change in specificity with CAD, weighted towards sensitivity) for detection of patients with polyps.

RESULTS

Inexperienced readers' per-patient sensitivity rose from 39.1% to 53.2% with CAD and specificity fell from 94.1% to 88.0%, both statistically significant. Experienced readers' sensitivity rose from 57.5% to 62.1% and specificity fell from 91.0% to 88.3%, both non-significant. Net benefit with CAD assistance was significant for inexperienced readers but not for experienced readers: 11.2% (95%CI 3.1% to 18.9%) versus 3.2% (95%CI -1.9% to 8.3%) respectively.

CONCLUSIONS

Concurrent CAD resulted in a significant net benefit when used by inexperienced readers to identify patients with polyps by CT colonography. The net benefit was nearly four times the magnitude of that observed for experienced readers. Experienced readers did not benefit significantly from concurrent CAD.

The effect of computer-aided detection markers on visual search and reader performance during concurrent reading of CT colonography

OBJECTIVE

We aimed to identify the effect of computer-aided detection (CAD) on visual search and performance in CT Colonography (CTC) of inexperienced and experienced readers.

METHODS

Fifteen endoluminal CTC examinations were recorded, each with one polyp, and two videos were generated, one with and one without a CAD mark. Forty-two readers (17 experienced, 25 inexperienced) interpreted the videos during infrared visual search recording. CAD markers and polyps were treated as regions of interest in data processing. This multi-reader, multi-case study was analysed using multilevel modelling.

RESULTS

CAD drew readers' attention to polyps faster, accelerating identification times: median 'time to first pursuit' was 0.48 s (IQR 0.27 to 0.87 s) with CAD, versus 0.58 s (IQR 0.35 to 1.06 s) without. For inexperienced readers, CAD also held visual attention for longer. All visual search metrics used to assess visual gaze behaviour demonstrated statistically significant differences when "with" and "without" CAD were compared. A significant increase in the number of correct polyp identifications across all readers was seen with CAD (74 % without CAD, 87 % with CAD; p < 0.001).

CONCLUSIONS

CAD significantly alters visual search and polyp identification in readers viewing three-dimensional endoluminal CTC. For polyp and CAD marker pursuit times, CAD generally exerted a larger effect on inexperienced readers.

KEY POINTS

• Visual gaze is attracted by computer-assisted detection (CAD) marks on polyps • Inexperienced readers' gaze is affected more by CAD than experienced readers. • CAD marks could mean that the unannotated endoluminal surface is relatively neglected. • Correct polyp identification is increased significantly by CAD.

CT colonography: accuracy, acceptance, safety and position in organised population screening

Colorectal cancer (CRC) is the second most common cancer and second most common cause of cancer-related deaths in Europe. The introduction of CRC screening programmes using stool tests and flexible sigmoidoscopy, have been shown to reduce CRC-related mortality substantially. In several European countries, population-based CRC screening programmes are ongoing or being rolled out. Stool tests like faecal occult blood testing are non-invasive and simple to perform, but are primarily designed to detect early invasive cancer. More invasive tests like colonoscopy and CT colonography (CTC) aim at accurately detecting both CRC and cancer precursors, thus providing for cancer prevention. This review focuses on the accuracy, acceptance and safety of CTC as a CRC screening technique and on the current position of CTC in organised population screening. Based on the detection characteristics and acceptability of CTC screening, it might be a viable screening test. The potential disadvantage of radiation exposure is probably overemphasised, especially with newer technology. At this time-point, it is not entirely clear whether the detection of extracolonic findings at CTC is of net benefit and is cost effective, but with responsible handling, this may be the case. Future efforts will seek to further improve the technique, refine appropriate diagnostic algorithms and study cost-effectiveness.

History, evolution, and current status of radiologic imaging tests for colorectal cancer screening

Colorectal cancer screening is thought to be an effective tool with which to reduce the mortality from colorectal cancer through early detection and removal of colonic adenomas and early colon cancers. In this article, we review the history, evolution, and current status of imaging tests of the colon-including single-contrast barium enema, double-contrast barium enema, computed tomographic (CT) colonography, and magnetic resonance (MR) colonography-for colorectal cancer screening. Despite its documented value in the detection of colonic polyps, the double-contrast barium enema has largely disappeared as a screening test because it is widely perceived as a labor-intensive, time-consuming, and technically demanding procedure. In the past decade, the barium enema has been supplanted by CT colonography as the major imaging test in colorectal cancer screening in the United States, with MR colonography emerging as another viable option in Europe. Although MR colonography does not require ionizing radiation, the radiation dose for CT colonography has decreased substantially, and regular screening with this technique has a high benefit-to-risk ratio. In recent years, CT colonography has been validated as an effective tool for use in colorectal cancer screening that is increasingly being disseminated.

Computed tomography colonography: ready for prime time for colon cancer screening?

In 2008, results from the landmark American College of Radiology Imaging Network (ACRIN) trial provided evidence supporting the use computed tomography colonography (CTC) as a comparable alternative to colonoscopy for colorectal cancer (CRC) screening. Subsequently, however, the United States Preventive Task Force decided against a recommendation in support of CTC for CRC screening. Following soon after, the Centers for Medicare and Medicaid Services (CMS) made noncoverage decision for the use of CTC in CRC screening. Since that decision, there have been a number of publications on CTC and CRC screening with a strong push from the radiology community to reassess CTC as a viable option. The purpose of this review was to address focused questions concerning the use of CTC in CRC screening, through an analysis of the available scientific evidence in an effort to provide recommendations for clinicians, patients, and payors who may evaluate the role of CTC for CRC screening.

Population screening for colorectal cancer by flexible sigmoidoscopy or CT colonography: study protocol for a multicenter randomized trial

BACKGROUND

Colorectal cancer (CRC) is the second most prevalent type of cancer in Europe. A single flexible sigmoidoscopy (FS) screening at around the age of 60 years prevents about one-third of CRC cases. However, FS screens only the distal colon, and thus mortality from proximal CRC is unaffected. Computed tomography colonography (CTC) is a highly accurate examination that allows assessment of the entire colon. However, the benefit of CTC testing as a CRC screening test is uncertain. We designed a randomized trial to compare participation rate, detection rates, and costs between CTC (with computer-aided detection) and FS as primary tests for population-based screening.

METHODS/DESIGN

An invitation letter to participate in a randomized screening trial comparing CTC versus FS will be mailed to a sample of 20,000 people aged 58 or 60 years, living in the Piedmont region and the Verona district of Italy. Individuals with a history of CRC, adenomas, inflammatory bowel disease, or recent colonoscopy, or with two first-degree relatives with CRC will be excluded from the study by their general practitioners. Individuals responding positively to the invitation letter will be then randomized to the intervention group (CTC) or control group (FS), and scheduled for the screening procedure. The primary outcome parameter of this part of the trial is the difference in advanced neoplasia detection between the two screening tests. Secondary outcomes are cost-effectiveness analysis, referral rates for colonoscopy induced by CTC versus FS, and the expected and perceived burden of the procedures. To compare participation rates for CTC versus FS, 2,000 additional eligible subjects will be randomly assigned to receive an invitation for screening with CTC or FS. In the CTC arm, non-responders will be offered fecal occult blood test (FOBT) as alternative screening test, while in the FS arm, non-responders will receive an invitation letter to undergo screening with either FOBT or CTC. Data on reasons for participation and non-participation will also be collected.

DISCUSSION

This study will provide reliable information concerning benefits and risks of the adoption of CTC as a mass screening intervention in comparison with FS. The trial will also evaluate the role of computer-aided detection in a screening setting.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT01739608.

Evaluation of computer-aided detection and diagnosis systems

Computer-aided detection and diagnosis (CAD) systems are increasingly being used as an aid by clinicians for detection and interpretation of diseases. Computer-aided detection systems mark regions of an image that may reveal specific abnormalities and are used to alert clinicians to these regions during image interpretation. Computer-aided diagnosis systems provide an assessment of a disease using image-based information alone or in combination with other relevant diagnostic data and are used by clinicians as a decision support in developing their diagnoses. While CAD systems are commercially available, standardized approaches for evaluating and reporting their performance have not yet been fully formalized in the literature or in a standardization effort. This deficiency has led to difficulty in the comparison of CAD devices and in understanding how the reported performance might translate into clinical practice. To address these important issues, the American Association of Physicists in Medicine (AAPM) formed the Computer Aided Detection in Diagnostic Imaging Subcommittee (CADSC), in part, to develop recommendations on approaches for assessing CAD system performance. The purpose of this paper is to convey the opinions of the AAPM CADSC members and to stimulate the development of consensus approaches and "best practices" for evaluating CAD systems. Both the assessment of a standalone CAD system and the evaluation of the impact of CAD on end-users are discussed. It is hoped that awareness of these important evaluation elements and the CADSC recommendations will lead to further development of structured guidelines for CAD performance assessment. Proper assessment of CAD system performance is expected to increase the understanding of a CAD system's effectiveness and limitations, which is expected to stimulate further research and development efforts on CAD technologies, reduce problems due to improper use, and eventually improve the utility and efficacy of CAD in clinical practice.