Computer-aided detection in computed tomography colonography: current status and problems with detection of early colorectal cancer

PURPOSE: The aim of this study was to evaluate the usefulness of computer-aided detection (CAD) in diagnosing early colorectal cancer using computed tomography colonography (CTC). MATERIALS AND METHODS: A total of 30 CTC data sets for 30 early colorectal cancers in 30 patients were retrospectively reviewed by three radiologists. After primary evaluation, a second reading was performed using CAD findings. The readers evaluated each colorectal segment for the presence or absence of colorectal cancer using five confidence rating levels. To compare the assessment results, the sensitivity and specificity with and without CAD were calculated on the basis of the confidence rating, and differences in these variables were analyzed by receiver operating characteristic (ROC) analysis. RESULTS: The average sensitivities for the detection without and with CAD for the three readers were 81.6% and 75.6%, respectively. Among the three readers, only one reader improved sensitivity with CAD compared to that without. CAD decreased specificity in all three readers. CAD detected 100% of protruding lesions but only 69.2% of flat lesions. On ROC analysis, the diagnostic performance of all three readers was decreased by use of CAD. CONCLUSION: Currently available CAD with CTC does not improve diagnostic performance for detecting early colorectal cancer. An improved CAD algorithm is required for detecting flat lesions and reducing the false-positive rate.     

Computer-aided detection in mammography

Mammographic film reading for breast screening is a highly demanding visual task involving a detailed visual search for signs of abnormality, which are infrequent and often small or subtle. False-negative cases, in which a cancer is missed by a film reader, are known to occur. Although double reading has proved effective in reducing errors, there is a national shortage of film readers in the screening programme, and recent extensions to the programme have exacerbated this problem. The use of computer-aided detection (CAD) systems could potentially provide a solution by improving individual performance to the extent that double reading is no longer necessary. In this paper, we describe how CAD works, review the relevant literature and examine the strengths and weaknesses of the approach.     

Computer-aided detection in CT colonography: initial clinical experience using a prototype system

Computer-aided detection (CAD) algorithms help to detect colonic polyps at CT colonography (CTC). The purpose of this study was to evaluate the accuracy of CAD versus an expert reader in CTC. One hundred forty individuals (67 men, 73 women; mean age, 59 years) underwent screening 64-MDCT colonography after full cathartic bowel cleansing without fecal tagging. One expert reader interpreted supine and prone scans using a 3D workstation with integrated CAD used as "second reader." The system's sensitivity for the detection of polyps, the number of false-positive findings, and its running time were evaluated. Polyps were classified as small (< or =5 mm), medium (6-9 mm), and large (> or =10 mm). A total of 118 polyps (small, 85; medium, 19; large, 14) were found in 56 patients. CAD detected 72 polyps (61%) with an average of 2.2 false-positives. Sensitivity was 51% (43/85) for small, 90% (17/19) for medium, and 86% (12/14) for large polyps. For all polyps, per-patient sensitivity was 89% (50/56) for the radiologist and 73% (41/56) for CAD. For large and medium polyps, per-patient sensitivity was 100% for the radiologist, and 96% for CAD. In conclusion, CAD shows high sensitivity in the detection of clinically significant polyps with acceptable false-positive rates.     

There's nothing plain about projection radiography! A discussion paper

ObjectivesUnlike the technological advances in cross-sectional imaging, the adoption of CR and DR has been relatively overlooked in terms of the additional radiographer skills and competences required for optimal practice. Furthermore, projection radiography is often referred to as basic, plain or other words suggesting simplicity or entry-level skill requirements. Radiographers’ professional identity is connected with the discourse expressed via the language used in daily practice and consequently, if the perception of projection radiography is regarded as simple practice not requiring much reflection or complex decision-making, apathy and carelessness may arise. The purpose of this narrative review was to raise projection radiography from its longstanding lowly place and re-position it as a specialist imaging field.Key findingsDanish pre-registration radiography curricula contain little mention of projection radiography and a low proportion (n = 17/144; 11.8%) of Danish radiography students chose to focus on projection radiography within publicly available BSc. theses between 2016 and 2020 as compared to topics related to CT and MRI (n = 60/144; 41.7%).ConclusionBy changing how we as the profession perceive the role and position of projection radiography, we can start to rebuild its lost prestige and demand a greater, more detailed and clinically relevant educational offering from academic partners. For this to commence, the language and terminology we use to describe ourselves and tasks undertaken must reflect the complexity of the profession.Implications for practiceRegardless of imaging modality, every patient should be assured that a radiographer with expertise in acquiring images of diagnostic quality undertakes their examination. Reclaiming the prestige of projection radiography may lead students and radiographers to recognize projection radiography as a demanding specialist field for the benefit of the patients.     

Computer-aided detection of lung nodules on multidetector CT in concurrent-reader and second-reader modes: A comparative study

Purpose

To compare the reading times and detection performances of radiologists in concurrent-reader and second-reader modes of computer-aided detection (CAD) for lung nodules on multidetector computed tomography (CT).

Materials and Methods

Fifty clinical multidetector CT datasets containing nodules up to 20 mm in diameter were retrospectively collected. For the detection and rating of non-calcified nodules larger than 4 mm in diameter, 6 radiologists (3 experienced radiologists and 3 resident radiologists) independently interpreted these datasets twice, once with concurrent-reader CAD and once with second-reader CAD. The reference standard of nodules in the datasets was determined by the consensus of two experienced chest radiologists. The reading times and detection performances in the two modes of CAD were statistically compared, where jackknife free-response receiver operating characteristic (JAFROC) analysis was used for the comparison of detection performances.

Results

Two hundreds and seven nodules constituted the reference standard. Reading time was significantly shorter in the concurrent-reader mode than in the second-reader mode, with the mean reading time for the 6 radiologists being 132 s with concurrent-reader CAD and 210 s with second-reader CAD (p < 0.01). JAFROC analysis revealed no significant difference between the detection performances in the two modes, with the average figure-of-merit value for the 6 radiologists being 0.70 with concurrent-reader CAD and 0.72 with second-reader CAD (p = 0.35).

Conclusion

In CAD for lung nodules on multidetector CT, the concurrent-reader mode is more time-efficient than the second-reader mode, and there can be no significant difference between the two modes in terms of detection performance of radiologists.     

Computer-aided polyp detection on CT colonography: Comparison of three systems in a high-risk human population

Purpose

To compare the detection performances of two commercial and one academic computer-aided diagnosis (CAD) systems for polyp detection on CT colonography (CTC) in a high-risk human population and to assess their detection characteristics.

Materials and methods

This retrospective study had institutional review board approval, but informed consent was waived. Sixty-eight patients who were suspected of having colonic polyps and scheduled for colonoscopic polyp removal were included. After CTC was performed using a 64-row MDCT, two commercial (PEV, CAR) and one academic (Hessian matrix-based) CAD systems were applied to each CTC dataset. Colonoscopy using the segmental unblinded technique was performed as a standard of reference. Per-polyp and per-patient sensitivities were calculated and compared for each CAD system. The mean number of false-positives (FPs) and false-negatives (FNs) was computed and the causes of all FPs and FNs were analyzed.

Results

A total of 151 polyps in 61 patients were detected (77 polyps <6 mm, 48 6-9.9 mm, 26 ≥ 10 mm). Per-polyp sensitivity for PEV, CAR, and Hessian matrix-based CAD were: 71.6%, 78.4%, and 83.8% for polyps ≥6 mm, and 88.5%, 96.2%, and 96.2% for polyps ≥10 mm. Per-patient sensitivity for polyps ≥6 mm was 80.4%, 89.1%, and 93.5%, and 87%, 95.7%, and 95.7% for polyps ≥10 mm, respectively. Per-polyp and per-patient sensitivities were not significantly different among the three CAD systems regardless of size threshold. Mean number of FPs was 6.9 for PEV, 7.3 for CAR, and 14 for Hessian matrix-based CAD. The most common cause of FPs were feces, followed by extracolonic findings, prominent folds and ileocecal valve, and rectal tube. The distribution of the causes of FPs was significantly different among the three systems.

Conclusion

Sensitivity of the three CAD systems for polyp detection was comparable regardless of the polyp size threshold; however, the number of FPs was higher in the Hessian matrix-based CAD. In addition, the distribution of the causes of FPs was significantly different among the three systems.     

Computer-aided detection in breast MRI: a systematic review and meta-analysis

Objectives

To evaluate the additional value of computer-aided detection (CAD) in breast MRI by assessing radiologists?? accuracy in discriminating benign from malignant breast lesions.

Methods

A literature search was performed with inclusion of relevant studies using a commercially available CAD system with automatic colour mapping. Two independent researchers assessed the quality of the studies. The accuracy of the radiologists?? performance with and without CAD was presented as pooled sensitivity and specificity.

Results

Of 587 articles, 10 met the inclusion criteria, all of good methodological quality. Experienced radiologists reached comparable pooled sensitivity and specificity before and after using CAD (sensitivity: without CAD: 89%; 95% CI: 78?C94%, with CAD: 89%; 95%CI: 81?C94%) (specificity: without CAD: 86%; 95% CI: 79?C91%, with CAD: 82%; 95% CI: 76?C87%). For residents the pooled sensitivity increased from 72% (95% CI: 62?C81%) without CAD to 89% (95% CI: 80?C94%) with CAD, however, not significantly. Concerning specificity, the results were similar (without CAD: 79%; 95% CI: 69?C86%, with CAD: 78%; 95% CI: 69?C84%).

Conclusions

CAD in breast MRI has little influence on the sensitivity and specificity of experienced radiologists and therefore their interpretation remains essential. However, residents or inexperienced radiologists seem to benefit from CAD concerning breast MRI evaluation.     

乳腺MR的计算机辅助检测:系统回顾和荟萃分析

目的通过评价放射科医师鉴别乳腺良、恶性病变的准确性来探讨乳腺MR的计算机辅助检测(CAD)的价值。方法进行相关的文献检索,包括所有商业许可运行的自动     

Automated mass detection in contrast-enhanced CT colonography: an approach based on contrast and volume

The purpose of this feasibility study was to design and test an algorithm for automating mass detection in contrast-enhanced CT colonography (CTC). Five patients with known colorectal masses underwent a pre-surgical contrast-enhanced (120 ml volume 1.6 g iodine/s injection rate, 60 s scan delay) CTC in high spatial resolution (16-slice CT: collimation: 16×0.75 mm, tablefeed: 24 mm/0.5 s, reconstruction increment: 0.5 mm). A CT-density- and volume-based algorithm searched for masses in the colonic wall, which was extracted before by segmenting and dilating the colonic air lumen and subtracting the inner air. A radiologist analyzed the detections and causes of false positives. All masses were detected, and false positives were easy to identify. Combining CT density with volume as a cut-off is a promising approach for automating mass detection that should be further refined and also tested in contrast-enhanced MR colonography. More information under .     

Computer-aided detection in direct digital full-field mammography: initial results

For the first time, full-field digital mammography (FFDM) allows computer-aided detection (CAD) analysis of directly acquired digital image data. The purpose of this study was to evaluate a CAD system in patients with histologically correlated breast cancer depicted with FFDM. Sixty-three cases of histologically proven breast cancer detected with FFDM (Senographe 2000D, GE Medical Systems, Buc, France) were analyzed using a CAD system (Image Checker V2.3, R2 Technology, Los Altos, Calif.). Fourteen of these malignancies were characterized as microcalcifications, 37 as masses, and 12 as both. The mammographic findings were categorized as BI-RADS 3 (n=5), BI-RADS 4 (n=17) and BI-RADS 5 (n=40). The sensitivity for malignant lesions and the rate of false-positive marks per image were calculated. The sensitivity and its 95% confidence interval (CI) were estimated. The sensitivity of the CAD R2 system in breast cancer seen on FFDM was 89% for microcalcifications [CI_95%=(70%; 98%)] and 81% for masses [CI_95%=(67%; 91%)]. As expected, the detection rate was higher in lesions categorized as BI-RADS 5 (37 of 40) compared with lesions categorized as BI-RADS 4 (11 of 17). In the group categorized as BI-RADS 3 the detection rate was 4 of 5 lesions; however, this group was very small. The rate of false-positive marks was 0.35 microcalcification marks/image and 0.26 mass marks/image. The overall rate of false-positive marks was 0.61 per image. CAD based on FFDM provides an optimized work flow. Results are equivalent to the results reported for CAD analysis of secondarily digitized image data. Sensitivity for microcalcifications is acceptable and for masses is low. The number of false-positive marks per image should be reduced. Electronic Publication     

不同大小和密度肺结节DR摄影:使用和不使用计算机辅助检测的比较

目的:评价DR摄影与计算机辅助检测对肺结节的大小和密度诊断测量的相关性.方法:选取177例(186个结节)经CT证实存在肺小结节的DR胸片和180例正常DR胸片,结节直径5～20mm.应用受试者操作特性曲线(ROC)分析未使用和使用CAD系统对不同大小(D)和密度肺结节检测结果的差异.结果:对于诊断3组不同大小(5 m...     

Computer-aided lung nodule detection in CT: results of large-scale observer test

RATIONALE AND OBJECTIVES: The objective is to study the incremental effects of using a computer-aided lung nodule detection (CAD) system on the performance of a large pool of observers. MATERIALS AND METHODS: A set of eight thin-section computed tomographic data sets with limited longitudinal coverage, containing a total of 22 lung nodules, was analyzed by using the automated nodule detection system. When applied to all eight cases, the CAD system alone achieved a detection rate of 86.4%, with 2.64 false-positive results per case. This study included 202 observers at a national radiology meeting: 39 thoracic radiologists, 95 non-thoracic radiologists, and 68 non-radiologists. Each participant read from one to eight cases in random order, first without and then with CAD system output available. Observer performance in nodule detection was measured before and after CAD was made available. Differences in performance of groups of observers before and after CAD were tabulated by mean, median, and SD in detection rate and number of false-positive results and tested by using nonparametric methods. RESULTS: In an analysis involving only the first randomly selected case read by all 202 participants, there were statistically significant increases in nodule detection rates and numbers of false-positive results for all types of observers. There was a significant difference in detection rates between radiologists and non-radiologists before CAD, but after CAD, there was no significant difference in detection rates between these observer types. In a second analysis involving 13 participants who read all eight cases, mean detection rates were 64.0% before CAD and 81.9% after CAD. Mean numbers of false-positive results were 0.144 per case before CAD and 0.173 after CAD. CONCLUSION: In a large observer study, use of a CAD system for nodule detection resulted in an incremental increase in detection rate, but also led to an increase in number of false-positive results. Also, CAD appears to be an equalizer of detection rates between observers of different levels of experience.     

Effects of incorrect computer-aided detection (CAD) output on human decision-making in mammography

RATIONALE AND OBJECTIVES: To investigate the effects of incorrect computer output on the reliability of the decisions of human users. This work followed an independent UK clinical trial that evaluated the impact of computer-aided detection(CAD) in breast screening. The aim was to use data from this trial to feed into probabilistic models (similar to those used in "reliability engineering") which would detect and assess possible ways of improving the human-CAD interaction. Some analyses required extra data; therefore, two supplementary studies were conducted. Study 1 was designed to elucidate the effects of computer failure on human performance. Study 2 was conducted to clarify unexpected findings from Study 1. MATERIALS AND METHODS: In Study 1, 20 film readers viewed 60 sets of mammograms (30 of which contained cancer) and provided "recall/no recall" decisions for each case. Computer output for each case was available to the participants. The test set was designed to contain an unusually large proportion (50%) of cancers for which CAD had generated incorrect output. In Study 2, 19 different readers viewed the same set of cases in similar conditions except that computer output was not available. RESULTS: The average sensitivity of readers in Study 1 (with CAD) was significantly lower than the average sensitivity of read-ers in Study 2 (without CAD). The difference was most marked for cancers for which CAD failed to provide correct prompting. CONCLUSION: Possible automation bias effects in CAD use deserve further study because they may degrade human decision-making for some categories of cases under certain conditions. This possibility should be taken into account in the assessment and design of CAD tools.     

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.     

Fully automated three-dimensional detection of polyps in fecal-tagging CT colonography

RATIONALE AND OBJECTIVES: The presence of opacified materials presents several technical challenges for automated detection of polyps in fecal-tagging computed tomography colonography (ftCTC), such as pseudo-enhancement and the distortion of the density, size, and shape of the observed lesions. We developed a fully automated computer-aided detection (CAD) scheme that addresses these issues in automated detection of polyps in ftCTC. MATERIALS AND METHODS: Pseudo-enhancement was minimized by use of an adaptive density correction (ADC) method. The presence of tagging was minimized by use of an adaptive density mapping (ADM) method. We also developed a new method for automated extraction of the colonic wall within air-filled and tagged regions. The ADC and ADM parameters were optimized by use of an anthropomorphic phantom. The CAD scheme was evaluated with 32+32 cases from two types of clinical ftCTC databases. The cases in database I had full cathartic cleansing and 40 polyps > or =6 mm, and the cases in database II had reduced cathartic cleansing and 44 polyps > or =6 mm. The by-polyp detection performance of the CAD scheme was evaluated by use of a leave-one-patient-out method with five features, and the results were compared with those of a conventional CAD scheme by use of free-response receiver operating characteristic curves. RESULTS: The CAD scheme detected 95% and 86% of the polyps > or =6 mm with 3.6 and 4.2 false positives per scan on average in databases I and II, respectively. For polyps > or =10 mm, the detection sensitivity was 94% in database I (with one missed hyperplastic polyp) and 100% in database II at the same false-positive rate. The detection sensitivity of the new CAD scheme was approximately 20% higher than that of the conventional CAD scheme. CONCLUSIONS: The results show that the CAD scheme developed in this study resolves the technical challenges introduced by fecal tagging, is applicable to a variety of colon preparation regimens, and provides a performance superior to that of conventional CAD schemes.     

Computer-aided detection of colonic polyps using low-dose CT acquisitions

RATIONALE AND OBJECTIVES: This report proposes an alternative method for the automatic detection of colonic polyps that is robust enough to be directly applicable on low-dose computed tomographic data. MATERIALS AND METHODS: The polyp modeling process takes into account both the gray-level appearance of polyps (intensity profiles) and their geometry (extended Gaussian images). Spherical harmonic decompositions are used for comparison purposes, allowing fast estimation of the similarity between a candidate and a set of previously computed models. Starting from the original raw data (acquired at 55 mA), five patient data sets (prone and supine scans) are reconstructed at different dose levels (to 5 mA) by using different kernel filters, slice overlaps, and increments. Additionally, the efficacy of applying an edge-preserving smoothing filter before detection is assessed. RESULTS: Although image quality decreases when decreasing acquisition milliamperes, all polyps greater than 6 mm are detected successfully, even at 15 mA. Although not important at high doses, smoothing improves detection results for ultra-low-dose (tube current<15 mA) data. CONCLUSION: The advantage of low-dose scans is a significant decrease in effective dose from 4.93 to 1.61 mSv while retaining high detection values, particularly important when thinking of population screening.     

A computer-aided detection system for rheumatoid arthritis MRI data interpretation and quantification of synovial activity

Rational and objective

Disease assessment and follow-up of rheumatoid arthritis (RA) patients require objective evaluation and quantification. Magnetic resonance imaging (MRI) has a large potential to supplement such information for the clinician, however, time spent on data reading and interpretation slow down development in this area. Existing scoring systems of especially synovitis are too rigid and insensitive to measure early treatment response and quantify inflammation. This study tested a novel automated, computer system for analysis of dynamic MRI data acquired from patients with RA, Dynamika-RA, which incorporates efficient data processing and analysis techniques.

Materials and methods

140 MRI scans from hands and wrists of 135 active RA patients and 5 healthy controls were processed using Dynamika-RA and evaluated with RAMRIS. To reduce patient motion artefacts, MRI data were processed using Dynamika-RA, which removed motion in 2D and 3D planes. Then synovial enhancement was visualised and qualified using a novel fully automated voxel-by-voxel analysis based algorithm. This algorithm was used to replace traditional region-of-interest (ROI) and subtraction methods, yielding observer independent quantitative results.

Results

Conventional scoring performed by an observer took 30-45 min per dataset. Dynamika-RA reduced motion artefacts, visualised inflammation and quantified disease activity in less than 3 min. Data processing allowed increasing signal to noise ratio by a factor 3. Due to fully automated procedure of data processing, there was no intertest variation in the results.

Conclusions

Algorithms incorporated into Dynamika-RA allow for the significant enhancement of data quality through eliminating motion artefacts and reduction of time for evaluation of synovial inflammation.