Effect of computer-aided detection as a second reader in multidetector-row CT colonography

Our purpose was to assess the effect of computer-aided detection (CAD) on lesion detection as a second reader in computed tomographic colonography, and to compare the influence of CAD on the performance of readers with different levels of expertise. Fifty-two CT colonography patient data-sets (37 patients: 55 endoscopically confirmed polyps ≥0.5 cm, seven cancers; 15 patients: no abnormalities) were retrospectively reviewed by four radiologists (two expert, two nonexpert). After primary data evaluation, a second reading augmented with findings of CAD (polyp-enhanced view, Siemens) was performed. Sensitivities and reading time were calculated for each reader without CAD and supported by CAD findings. The sensitivity of expert readers was 91% each, and of nonexpert readers, 76% and 75%, respectively, for polyp detection. CAD increased the sensitivity of expert readers to 96% (P = 0.25) and 93% (P = 1), and that of nonexpert readers to 91% (P = 0.008) and 95% (P = 0.001), respectively. All four readers diagnosed 100% of cancers, but CAD alone only 43%. CAD increased reading time by 2.1 min (mean). CAD as a second reader significantly improves sensitivity for polyp detection in a high disease prevalence population for nonexpert readers. CAD causes a modest increase in reading time. CAD is of limited value in the detection of cancer.     

Colonic polyps: complementary role of computer-aided detection in CT colonography

PURPOSE: To apply a computer-aided detection (CAD) algorithm to supine and prone multisection helical computed tomographic (CT) colonographic images to confirm if there is any added benefit provided by CAD over that of standard clinical interpretation. MATERIALS AND METHODS: CT colonography (with patients in both supine and prone positions) was performed with a multisection helical CT scanner in 40 asymptomatic high-risk patients. There were two consecutive series of patients, 20 of whom had at least one polyp 1.0 cm in size or larger and 20 of whom had normal colons at conventional colonoscopy performed the same day. The CT colonographic images were interpreted with an automated CAD algorithm and by two radiologists who were blinded to colonoscopy findings. RESULTS: For 25 polyps at least 1.0 cm in size ("large" polyps), sensitivity for detection by at least one radiologist was 48% (12 of 25). The sensitivity of CAD for detecting large polyps was also 48% (12 of 25), but the CAD algorithm detected four of 13 large polyps that were not detected by either radiologist (31%, 95% two-sided CI: 9, 61), increasing the potential sensitivity to 64% (16 of 25). For polyps identifiable retrospectively, sensitivity of CAD was 67% (12 of 18), and sensitivity of the combination of detection with the CAD algorithm or by at least one radiologist was 89% (16 of 18). There were an average of 11 false-positive detections per patient for CAD. CONCLUSION: In this series of patients in whom radiologists had difficulties detecting polyps (compared with sensitivities of 75%-90% reported in the literature), this CAD algorithm played a complementary role to conventional interpretation of CT colonographic images by detecting a number of large polyps missed by trained observers.     

CT colonography: effect of experience and training on reader performance

The purpose of this paper was to investigate the effect of radiologist experience and increasing exposure to CT colonography on reader performance. Three radiologists of differing general experience (consultant, research fellow, trainee) independently analysed 100 CT colonographic datasets. Readers had no prior experience of CT colonography and received feedback and training after the first 50 cases from an independent experienced radiologist. Diagnostic performance and reporting times were compared for the first and second 50 datasets and compared with the results of a radiologist experienced in CT colonography. Before training only the consultant reader achieved statistical equivalence with the reference standard for detection of larger polyps. After training, detection rates ranged between 25 and 58% for larger polyps. Only the trainee significantly improved after training (P=0.007), with performance of other readers unchanged or even worse. Reporting times following training were reduced significantly for the consultant and fellow (P<0.001 and P=0.03, respectively), but increased for the trainee (P<0.001). In comparison to the consultant reader, the odds of detection of larger polyps was 0.36 (CI 0.16, 0.82) for the fellow and 0.36 (CI 0.14, 0.91) for the trainee. There is considerable variation in the ability to report CT colonography. Prior experience in gastrointestinal radiology is a distinct advantage. Competence cannot be assumed even after directed training via a database of 50 cases.     

CT colonography: computer-aided detection of morphologically flat T1 colonic carcinoma

The purpose was to evaluate the ability of computer-aided detection (CAD) software to detect morphologically flat early colonic carcinoma using CT colonography (CTC). Twenty-four stage T1 colonic carcinomas endoscopically classified as flat (width over twice height) were accrued from patients undergoing staging CTC. Tumor location was annotated by three experienced radiologists in consensus aided by the endosocpic report. CAD software was then applied at three settings of sphericity (0, 0.75, and 1). Computer prompts were categorized as either true positive (overlapping tumour boundary) or false positive. True positives were subclassified as focal or non focal. The 24 cancers were endoscopically classified as type IIa (n=11) and type IIa+IIc (n=13). Mean size (range) was 27 mm (7-70 mm). CAD detected 20 (83.3%), 17 (70.8%), and 13 (54.1%) of the 24 cancers at filter settings of 0, 0.75, and 1, respectively with 3, 4, and 8 missed cancers of type IIa, respectively. The mean total number of false-positive CAD marks per patient at each filter setting was 36.5, 21.1, and 9.5, respectively, excluding polyps. At all settings, >96.1% of CAD true positives were classified as focal. CAD may be effective for the detection of morphologically flat cancer, although minimally raised laterally spreading tumors remain problematic.     

Automated segmentation of colonic walls for computerized detection of polyps in CT colonography

PURPOSE: A new method for fully automated segmentation of the colonic walls in volumetric CT data was developed for limitation of the search space in computerized detection of polyps. METHOD: For reliable segmentation, an anatomy-oriented approach was used, in which several anatomical structures are segmented in addition to the colon for utilization of their properties. RESULTS: The segmentation method was validated by use of 14 data sets, consisting of cases positive for colonic polyps. We found that the segmented colonic walls included all of the polyps. A subjective rating of the results was performed based on several criteria for visualization of anatomic detail of the colonic wall and mucosal surface. Except for a few cases in which insufflation of the colon was insufficient, all of the results included >95% of the colonic walls. CONCLUSION: This method for colonic wall segmentation is reliable and the segmentation results are applicable in both visualization of the colon and computer-aided diagnosis in the detection of polyps in CT colonography.     

Automated knowledge-guided segmentation of colonic walls for computerized detection of polyps in CT colonography

PURPOSE: We have developed a novel automated technique for segmenting colonic walls for the application of computer-aided polyp detection in CT colonography. In particular, the technique was designed to minimize the presence of extracolonic components, such as small bowel, in the segmented colon. METHODS: The segmentation technique combines an improved version of our previously reported anatomy-oriented colon segmentation technique with a colon-based analysis step that performs self-adjusting volume-growing within the colonic lumen. Extracolonic components are eliminated by intersecting of the resulting two segmentations, so that the colonic walls remain in the intersection. The technique was evaluated on 88 CT colonography datasets. The colon segmentations were evaluated subjectively by four radiologists, as well as objectively by performance of an automated polyp detection on the segmentation. For comparison, the tests were also performed for the anatomy-oriented colon segmentation technique. RESULTS: On average, the technique covered 98% of the visible colonic walls. Approximately 50% of the extracolonic components remaining in the anatomy-oriented segmentation were removed, but 10-15% of the segmentation still contained extracolonic components. The dataset-based false-positive rate of the automated polyp detection was improved by 10% without compromising the 100% case-based sensitivity, and the case-based false-positive rate was improved by 15% over the previous false-positive rate. CONCLUSIONS: The technique segments practically all of the colonic walls in the region of diagnostic quality with a large reduction in the amount of extracolonic components over our previously used technique. The new segmentation improves the specificity of our computer-aided polyp detection scheme significantly without any degradation in detection sensitivity.     

计算机辅助检测在结肠低剂量多层螺旋CT成像中的应用

目的 评估计算机辅助检测系统(CAD)设置不同的检测过滤器数值(SFV)时,在低剂量MSCT成像中对结肠病变的检测能力.方法 56例结肠癌和(或)结肠息肉患者行MSCT结肠成像扫描,依据结肠镜和外科手术结果,将病变分为4组:结肠癌、最长径≥10.0mm息肉、最长径5.1～9.9 mm息肉和最长径≤5.0 mm息肉,之后确定病变在CT图像上的部位及大小,作为评估结肠CAD系统检测病变的金标准.将CAD系统的SFV设为0.25、0.50、0.75和1.00共4个等级,分别检测CT结肠成像图像,记录CAD标注出的病灶的部位和大小,根据上述金标准评估CAD系统对结肠病变的检出率,采用x2检验比较不同SFV设置时CAD对各组病变的检出率.结果 56例患者共有159个阳性病灶,其中结肠癌为44个,最长径≥10.0mm息肉45个,最长径5.1～9.9 mm息肉32个,最长径≤5.0 mm息肉38个.将结肠CAD系统SFV分别设置为0.25、0.50、0.75和1.00时,病灶的检出率分别85.5%(136/159)、85.5%(136/159)、79.2%(126/159)和56.0%(89/159).SFV为0.25和0.50时,与SFV为1.00时,CAD对病灶检出率的差异有统计学意义(P＜0.05).随着SFV数值的减低,病灶的检出率增高,假阳性数增加,但91.4%(138/151)～93.9%(31/33)的假阳性病灶很容易识别,仅有6.1%(2/33)～8.6%(13/151)的假阳性病灶,需借助MPR和3D仿真内镜识别.结论 在低剂量MSCT结肠成像中,结肠CAD系统可获得满意的病灶检出率,可调节CAD系统SFV数值,以便满足不同经验阅片者的需求.     

Multiple neural network classification scheme for detection of colonic polyps in CT colonography data sets

RATIONALE AND OBJECTIVES: A new classification system for colonic polyp detection, designed to increase sensitivity and reduce the number of false-positive findings with computed tomographic colonography, was developed and tested in this study. MATERIALS AND METHODS: The system involves classification by a committee of neural networks (NNs), each using largely distinct subsets of features selected from a general set. Back-propagation NNs trained with the Levenberg-Marquardt algorithm were used as primary classifiers (committee members). The set of features included region density, Gaussian and mean curvature and sphericity, lesion size, colon wall thickness, and the means and standard deviations of all of these values. Subsets of variables were initially selected because of their effectiveness according to training and test sample misclassification rates. The final decision for each case is based on the majority vote across the networks and reflects the weighted votes of all networks. The authors also introduce a smoothed cross-validation method designed to improve estimation of the true misclassification rates by reducing bias and variance. RESULTS: This committee method reduced the false-positive rate by 36%, a clinically meaningful reduction, and improved sensitivity by an average of 6.9% compared with decisions made by any single NN. The overall sensitivity and specificity were 82.9% and 95.3%, respectively, when sensitivity was estimated by means of smoothed cross-validation. CONCLUSION: The proposed method of using multiple classifiers and majority voting is recommended for classification tasks with large sets of input features, particularly when selected feature subsets may not be equally effective and do not provide satisfactory true- and false-positive rates. This approach reduces variance in estimates of misclassification rates.     

CT结肠成像影像应用附加与同时计算机辅助诊断提高效益的多观察者研究

目的在计算机辅助诊断(CAD)系统作为附加或同时阅片手段时,对阅片人CT结肠成像影像诊断水平的改变(如果有)进行定量分析。材料与方法在得到机构审查委     

Computed tomography colonography: feasibility of computer-aided polyp detection in a "first reader" paradigm

OBJECTIVE:: To determine the feasibility of a computer-aided detection (CAD) algorithm as the "first reader" in computed tomography colonography (CTC). METHODS:: In phase 1 of a 2-part blind trial, we measured the performance of 3 radiologists reading 41 CTC studies without CAD. In phase 2, readers interpreted the same cases using a CAD list of 30 potential polyps. RESULTS:: Unassisted readers detected, on average, 63% of polyps > or =10 mm in diameter. Using CAD, the sensitivity was 74% (not statistically different). Per-patient analysis showed a trend toward increased sensitivity for polyps > or =10 mm in diameter, from 73% to 90% with CAD (not significant) without decreasing specificity. Computer-aided detection significantly decreased interobserver variability (P = 0.017). Average time to detection of the first polyp decreased significantly with CAD, whereas total reading case reading time was unchanged. CONCLUSION:: Computer-aided detection as a first reader in CTC was associated with similar per-polyp and per-patient detection sensitivity to unassisted reading. Computer-aided detection decreased interobserver variability and reduced the time required to detect the first polyp.