Virtual colonoscopy: effect of computer-assisted detection (CAD) on radiographer performance

New definitions of human lymphoid and follicular cell entities in lymphatic tissue by machine learning

Histological sections of the lymphatic system are usually the basis of static (2D) morphological investigations. Here, we performed a dynamic (4D) analysis of human reactive lymphoid tissue using confocal fluorescent laser microscopy in combination with machine learning. Based on tracks for T-cells (CD3), B-cells (CD20), follicular T-helper cells (PD1) and optical flow of follicular dendritic cells (CD35), we put forward the first quantitative analysis of movement-related and morphological parameters within human lymphoid tissue. We identified correlations of follicular dendritic cell movement and the behavior of lymphocytes in the microenvironment. In addition, we investigated the value of movement and/or morphological parameters for a precise definition of cell types (CD clusters). CD-clusters could be determined based on movement and/or morphology. Differentiating between CD3- and CD20 positive cells is most challenging and long term-movement characteristics are indispensable. We propose morphological and movement-related prototypes of cell entities applying machine learning models. Finally, we define beyond CD clusters new subgroups within lymphocyte entities based on long term movement characteristics. In conclusion, we showed that the combination of 4D imaging and machine learning is able to define characteristics of lymphocytes not visible in 2D histology.

A comparison of computer-assisted detection (CAD) programs for the identification of colorectal polyps: performance and sensitivity analysis, current limitations and practical tips for radiologists

AIM

To directly compare the accuracy and speed of analysis of two commercially available computer-assisted detection (CAD) programs in detecting colorectal polyps.

MATERIALS AND METHOD

In this retrospective single-centre study, patients who had colorectal polyps identified on computed tomography colonography (CTC) and subsequent lower gastrointestinal endoscopy, were analysed using two commercially available CAD programs (CAD1 and CAD2). Results were compared against endoscopy to ascertain sensitivity and positive predictive value (PPV) for colorectal polyps. Time taken for CAD analysis was also calculated.

RESULTS

CAD1 demonstrated a sensitivity of 89.8%, PPV of 17.6% and mean analysis time of 125.8 seconds. CAD2 demonstrated a sensitivity of 75.5%, PPV of 44.0% and mean analysis time of 84.6 seconds.

CONCLUSION

The sensitivity and PPV for colorectal polyps and CAD analysis times can vary widely between current commercially available CAD programs. There is still room for improvement. Generally, there is a trade-off between sensitivity and PPV, and so further developments should aim to optimise both. Information on these factors should be made routinely available, so that an informed choice on their use can be made. This information could also potentially influence the radiologist's use of CAD results.

A robust and efficient approach to detect 3D rectal tubes from CT colonography

PURPOSE

The rectal tube (RT) is a common source of false positives (FPs) in computer-aided detection (CAD) systems for CT colonography. A robust and efficient detection of RT can improve CAD performance by eliminating such "obvious" FPs and increase radiologists' confidence in CAD.

METHODS

In this paper, we present a novel and robust bottom-up approach to detect the RT. Probabilistic models, trained using kernel density estimation on simple low-level features, are employed to rank and select the most likely RT tube candidate on each axial slice. Then, a shape model, robustly estimated using random sample consensus (RANSAC), infers the global RT path from the selected local detections. Subimages around the RT path are projected into a subspace formed from training subimages of the RT. A quadratic discriminant analysis (QDA) provides a classification of a subimage as RT or non-RT based on the projection. Finally, a bottom-top clustering method is proposed to merge the classification predictions together to locate the tip position of the RT.

RESULTS

Our method is validated using a diverse database, including data from five hospitals. On a testing data with 21 patients (42 volumes), 99.5% of annotated RT paths have been successfully detected. Evaluated with CAD, 98.4% of FPs caused by the RT have been detected and removed without any loss of sensitivity.

CONCLUSIONS

The proposed method demonstrates a high detection rate of the RT path, and when tested in a CAD system, reduces FPs caused by the RT without the loss of sensitivity.

Effect of computer-aided detection for CT colonography in a multireader, multicase trial

PURPOSE

To assess the effect of using computer-aided detection (CAD) in second-read mode on readers' accuracy in interpreting computed tomographic (CT) colonographic images.

MATERIALS AND METHODS

The contributing institutions performed the examinations under approval of their local institutional review board, with waiver of informed consent, for this HIPAA-compliant study. A cohort of 100 colonoscopy-proved cases was used: In 52 patients with findings positive for polyps, 74 polyps of 6 mm or larger were observed in 65 colonic segments; in 48 patients with findings negative for polyps, no polyps were found. Nineteen blinded readers interpreted each case at two different times, with and without the assistance of a commercial CAD system. The effect of CAD was assessed in segment-level and patient-level receiver operating characteristic (ROC) curve analyses.

RESULTS

Thirteen (68%) of 19 readers demonstrated higher accuracy with CAD, as measured with the segment-level area under the ROC curve (AUC). The readers' average segment-level AUC with CAD (0.758) was significantly greater (P = .015) than the average AUC in the unassisted read (0.737). Readers' per-segment, per-patient, and per-polyp sensitivity for all polyps of 6 mm or larger was higher (P < .011, .007, .005, respectively) for readings with CAD compared with unassisted readings (0.517 versus 0.465, 0.521 versus 0.466, and 0.477 versus 0.422, respectively). Sensitivity for patients with at least one large polyp of 10 mm or larger was also higher (P < .047) with CAD than without (0.777 versus 0.743). Average reader sensitivity also improved with CAD by more than 0.08 for small adenomas. Use of CAD reduced specificity of readers by 0.025 (P = .05).

CONCLUSION

Use of CAD resulted in a significant improvement in overall reader performance. CAD improves reader sensitivity when measured per segment, per patient, and per polyp for small polyps and adenomas and also reduces specificity by a small amount.

ACR Colon Cancer Committee white paper: status of CT colonography 2009

PURPOSE

To review the current status and rationale of the updated ACR practice guidelines for CT colonography (CTC).

METHODS

Clinical validation trials in both the United States and Europe are reviewed. Key technical aspects of the CTC examination are emphasized, including low-dose protocols, proper insufflation, and bowel preparation. Important issues of implementation are discussed, including training and certification, definition of the target lesion, reporting of colonic and extracolonic findings, quality metrics, reimbursement, and cost-effectiveness.

RESULTS

Successful validation trials in screening cohorts both in the United States with ACRIN and in Germany demonstrated sensitivity > or = 90% for patients with polyps >10 mm. Proper technique is critical, including low-dose techniques in screening cohorts, with an upper limit of the CT dose index by volume of 12.5 mGy per examination. Training new readers includes the requirement of interactive workstation training with 2-D and 3-D image display techniques. The target lesion is defined as a polyp > or = 6 mm, consistent with the American Cancer Society joint guidelines. Five quality metrics have been defined for CTC, with pilot data entered. Although the CMS national noncoverage decision in May 2009 was a disappointment, multiple third-party payers are reimbursing for screening CTC. Cost-effective modeling has shown CTC to be a dominant strategy, including in a Medicare cohort.

CONCLUSION

Supported by third-party payer reimbursement for screening, CTC will continue to further transition into community practice and can provide an important adjunctive examination for colorectal screening.

Computer-aided detection (CAD) as a second reader using perspective filet view at CT colonography: effect on performance of inexperienced readers

AIM

To evaluate whether computer-aided detection (CAD) as a second reader using perspective filet view [three-dimensional (3D) filet] improves the performance of inexperienced readers at computed tomography colonography (CTC) compared with unassisted 3D filet and unassisted two-dimensional (2D) CTC.

MATERIAL AND METHODS

Fifty symptomatic patients underwent CTC and same-day colonoscopy with segmental unblinding. Two inexperienced readers read the CTC studies on 3D filet and 2D several weeks apart. Four months later, readers re-read the cases only evaluating CAD marks using 3D filet. Suspicious CAD marks not previously described on 3D filet were recorded. Jackknife free-response receiver operating characteristic (JAFROC-1) analysis was used to compare the observers' performances in detecting lesions with 3D filet, 2D and 3D filet with CAD.

RESULTS

One hundred and three lesions > or =3mm were detected at colonoscopy with segmental unblinding. CAD alone had a sensitivity of 73% (75/103) at a mean false-positive rate per patient of 12.8 in supine and 11.4 in prone. For inexperienced readers sensitivities with 3D filet with CAD were 58% (60/103) and 48% (50/103) with an improvement of 14-16 percentage points (p<0.05) compared with 2D and of 10-11 percentage points (p<0.05) compared with 3D filet. For inexperienced readers, the false-positive rate was 25-41% and 71-200% higher with 3D filet with CAD compared with 3D filet and 2D, respectively. JAFROC-1 analysis showed no significant differences in per-lesion overall performance among reading modes (p=0.8).

CONCLUSION

CAD applied as a second reader using 3D filet increased both sensitivity and the number of false positives by inexperienced readers compared with 3D filet and 2D, thus not improving overall performance, i.e., the ability to distinguish between lesions and non-lesions.

How should we train capsule endoscopy? A pilot study of performance changes during a structured capsule endoscopy training program

There are no validated training/accreditation guidelines for capsule endoscopy. We assessed the utility of a structured training program on two experienced gastroenterologists and one endoscopy nurse. Validated studies were standardized for difficulty in blocks of three. Trainees completed a standardized data sheet for each study reported (12 studies for the physicians, 22 studies for the nurse). After each block the trainer graded performance and highlighted learning points. Statistical analysis was performed. Physician trainees accurately identified landmarks throughout, while the nurse made errors even at the end of training. Improvement in lesion detection and diagnostic accuracy improved in the nurse, but in only one of the physician trainees, highlighting the variability in learning curves. Overall performance improved in all trainees but was most marked for the nurse trainee (correlation coefficient 0.41, P = 0.06). Improvements in lesion recognition and diagnosis can be demonstrated in senior trainees and nurses following a structured training program; however, there is considerable variability.