Phase- and size-adjusted CT cut-off for differentiating neoplastic lesions from normal colon in contrast-enhanced CT colonography

Luboldt, Wolfgang; Kroll, Manuela; Wetter, Axel; Toussaint, T. L.; Hoepffner, N.; Holzer, Katharina; Vogl, Thomas J.

doi:10.1007/s00330-004-2467-1

Cited by 6 publications

(2 citation statements)

References 26 publications

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“…CT colonography is a novel technique that visualizes the endoluminal surface of the air-filled distended colon in patients who are unable to undergo conventional colonoscopy or when the endoscopist cannot reach the cecum in a well-prepared colon [9,26,29] and when adjusted cut-off values for phase and size in CT colonography have been suggested for the differentiation of neoplastic lesions from normal colon [27]. A colon dissection approach may also provide a significant time advantage for the evaluation of CT colonography, while maintaining a high sensitivity [28].…”

Section: Discussionmentioning

confidence: 99%

The diagnostic value of multiplanar reconstruction on MDCT colonography for the preoperative staging of colorectal cancer

et al. 2006

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The purpose of this study was to determine whether multiplanar reconstruction (MPR) images can improve the accuracy of MDCT-based colorectal cancer preoperative staging by receiver-operating characteristic (ROC) analysis. Fifty-five patients with colorectal cancer underwent contrast-enhanced CT colonography using an 8- or 16-row scanner. Two separate interval reviews of the axial MDCT datasets with/without MPR images (coronal and sagittal) were performed independently by two radiologists blinded to both the colonoscopic and histopathologic results. At each review session, the radiologists were asked to determine the colorectal cancer TNM stage within the context of differentiating < or =T3 from T4, N0 from > or =N1 and M0 from M1 using a five-point confidence scale. The radiologists' performance for staging the colorectal cancer using axial CT datasets with/without MPR images was evaluated using ROC analysis. Sensitivities, specificities and interobserver agreement were assessed. When MPR images were added, significant improvement was achieved by both radiologists for differentiating N0 from > or =N1 in terms of both A(Z) (0.651 to 0.769; 0.573 to 0.713) and specificity (26.7 to 69.2%; 23.1 to 76.9%) (P<0.05). For T staging, ROC analysis failed to show a significant improvement in terms of differentiating < or =T3 from T4 for either radiologist (P>0.05), but a significant improvement in the specificity (70 to 90%; 80 to 92%) was achieved by one radiologist (P<0.05). In terms of the M staging, a significant improvement in the Az (0.844 to 0.996) was observed for the combined interpretation of the axial and MPR images by one radiologist (P<0.05). Furthermore, substantial or almost perfect interobserver agreement was achieved for all TNM stagings for the combined interpretations (kappa=0.641-0.866), whereas only fair to substantial agreement was achieved for the axial images alone (kappa=0.337-0.707). In conclusion, the combined interpretation of the axial and MPR MDCT images significantly improved the local staging of colorectal cancer compared with assessments based on axial images alone.

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Section: Discussionmentioning

confidence: 99%

The diagnostic value of multiplanar reconstruction on MDCT colonography for the preoperative staging of colorectal cancer

et al. 2006

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“…By deviating from the screening purpose, efforts have been devoted to differentiating neoplastic from non-neoplastic lesions (polyps and masses) by the use of intravenous (IV) contrast-enhanced CTC protocol [20, 23, 24, 41]. The gain by the IV-contrast-enhanced CT image textures is at the cost of the complication of IV related procedure, which would compromise the screening purpose.…”

Section: Discussion and Future Work Conclusionmentioning

confidence: 99%

Texture Feature Extraction and Analysis for Polyp Differentiation via Computed Tomography Colonography

Zhang

Song

et al. 2016

IEEE Trans. Med. Imaging

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Image textures in computed tomography colonography (CTC) have great potential for differentiating non-neoplastic from neoplastic polyps and thus can advance the current CTC detection-only paradigm to a new level toward optimal polyp management to prevent the deadly colorectal cancer. However, image textures are frequently compromised due to noise smoothing and other error-correction operations in most CT image reconstructions. Furthermore, because of polyp orientation variation in patient space, texture features extracted in that space can vary accordingly, resulting in variable results. To address these issues, this study proposes an adaptive approach to extract and analyze the texture features for polyp differentiation. Firstly, derivative operations are performed on the CT intensity image to amplify the textures, e.g. in the 1st order derivative (gradient) and 2nd order derivative (curvature) images, with adequate noise control. Then the Haralick co-occurrence matrix (CM) is used to calculate texture measures along each of the 13 directions (defined by the 1st and 2nd order image voxel neighbors) through the polyp volume in the intensity, gradient and curvature images. Instead of taking the mean and range of each CM measure over the 13 directions as the so-called Haralick texture features, the Karhunen-Loeve transform is performed to map the 13 directions into an orthogonal coordinate system where all the CM measures are projected onto the new coordinates so that the resulted texture features are less dependent on the polyp spatial orientation variation. While the ideas for amplifying textures and stabilizing spatial variation are simple, their impacts are significant for the task of differentiating non-neoplastic from neoplastic polyps as demonstrated by experiments using 384 polyp datasets, of which 52 are non-neoplastic polyps and the rest are neoplastic polyps. By the merit of area under the curve of receiver operating characteristic, the innovative ideas achieved differentiation capability of 0.8016, indicating the feasibility of advancing CTC toward personal healthcare for preventing colorectal cancer.

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

et al. 2004

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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: 16x0.75 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 http://www.screening.info.

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Phase- and size-adjusted CT cut-off for differentiating neoplastic lesions from normal colon in contrast-enhanced CT colonography

Cited by 6 publications

References 26 publications

The diagnostic value of multiplanar reconstruction on MDCT colonography for the preoperative staging of colorectal cancer

The diagnostic value of multiplanar reconstruction on MDCT colonography for the preoperative staging of colorectal cancer

Texture Feature Extraction and Analysis for Polyp Differentiation via Computed Tomography Colonography

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

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