Volumetric Textural Analysis of Colorectal Masses at CT Colonography

Pooler, B. Dustin; Lubner, Meghan G.; Theis, Jake R.; Halberg, Richard B.; Zhang, Liang; Pickhardt, Perry J.

doi:10.1016/j.acra.2018.03.002

Cited by 11 publications

(4 citation statements)

References 33 publications

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“…Certain volumetric textural features are predictive of polyp histology. The VTA demonstrated similar accuracy to human readers in differentiating between benign and malignant lesions ≥3 cm [11]. Further investigation and application of this technology in smaller lesions could help select those patients that should undergo endoscopic or operative intervention.…”

Section: Figure 3: Cecectomy Specimen With Intraluminal Mass Discussionmentioning

confidence: 76%

Appendiceal Inversion Presenting as a Cecal Polypoid Mass on Screening Colonoscopy: A Case Report and Review of Available Diagnostic Adjuncts to Differentiate Benign From Malignant Colorectal Pathology

Axentiev¹,

Shehzad²,

Bernescu³

2023

Cureus

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Appendiceal inversion is uncommon. It may be a benign finding or seen in association with malignant pathology. When detected, it masquerades as a cecal polyp which poses a diagnostic dilemma with malignancy in the differential. In this report, we highlight a case of a 51-year-old patient with an extensive surgical history as a newborn in the setting of omphalocele and intestinal malrotation, who was found to have a 4 cm cecal polypoid growth on screening colonoscopy. He underwent a cecectomy for tissue diagnosis. Ultimately, the polyp was found to be an inverted appendix without evidence of malignancy. Currently, suspicious colorectal lesions which cannot be removed by polypectomy are primarily addressed with surgical excision. We reviewed the literature for available diagnostic adjuncts to better differentiate benign from malignant colorectal pathology. The application of advanced imaging and molecular technology will allow for improved diagnostic accuracy and subsequent operative planning.

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Section: Figure 3: Cecectomy Specimen With Intraluminal Mass Discussionmentioning

confidence: 76%

Appendiceal Inversion Presenting as a Cecal Polypoid Mass on Screening Colonoscopy: A Case Report and Review of Available Diagnostic Adjuncts to Differentiate Benign From Malignant Colorectal Pathology

Axentiev¹,

Shehzad²,

Bernescu³

2023

Cureus

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“…The human experts are experienced radiologists with 15–35 years of clinical practice in their field. They had all the visualization tools in current clinical setting to exam each polyp, and achieved AUC values of 0.869, 0.926, and 0.960, respectively 39 . Our dynamic lesion model, called AlphaPolyp here, outperformed all three radiologists by AUC score of 0.986.…”

Section: Experiments and Resultsmentioning

confidence: 99%

A dynamic lesion model for differentiation of malignant and benign pathologies

Cao

Zhang

Gao

et al. 2021

Sci Rep

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Malignant lesions have a high tendency to invade their surrounding environment compared to benign ones. This paper proposes a dynamic lesion model and explores the 2nd order derivatives at each image voxel, which reflect the rate of change of image intensity, as a quantitative measure of the tendency. The 2nd order derivatives at each image voxel are usually represented by the Hessian matrix, but it is difficult to quantify a matrix field (or image) through the lesion space as a measure of the tendency. We conjecture that the three eigenvalues contain important information of the Hessian matrix and are chosen as the surrogate representation of the Hessian matrix. By treating the three eigenvalues as a vector, called Hessian vector, which is defined in a local coordinate formed by three orthogonal Hessian eigenvectors and further adapting the gray level occurrence computing method to extract the vector texture descriptors (or measures) from the Hessian vector, a quantitative presentation for the dynamic lesion model is completed. The vector texture descriptors were applied to differentiate malignant from benign lesions from two pathologically proven datasets: colon polyps and lung nodules. The classification results not only outperform four state-of-the-art methods but also three radiologist experts.

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“…The last step in Fig. 1 is to perform CNN based polyp classification using the generated 3D level (13 directional) GLCM volume image, which has the dimension (32,32,13). A multichannel network structure was designed, which takes each directional GLCM (a 2D image) as one input channel as shown in Fig.1 (Step 3).…”

Section: Cnn Based Polyp Classificationmentioning

confidence: 99%

“…As a screening option, CTC provides fully three-dimensional (3D) image data for volumetric-based polyp detection by either radiologist experts or computer-aided detection (CADe) [6][7][8][9][10]. Previous investigations for volumetric-based polyp diagnosis by either radiologist experts or computer-aided diagnosis (CADx) have shown promising results [11][12][13]. The volumetric-based CADe and CADx technologies will not only speed up the radiologist's examination, but also increase their confidence in decision making with the fully 3D information.…”

Section: Introductionmentioning

confidence: 99%

3D-GLCM CNN: A 3-Dimensional Gray-Level Co-Occurrence Matrix-Based CNN Model for Polyp Classification via CT Colonography

Tan

Gao

Zhang

et al. 2020

IEEE Trans. Med. Imaging

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Accurately classifying colorectal polyps, or differentiating malignant from benign ones, has a significant clinical impact on early detection and identifying optimal treatment of colorectal cancer. Convolution neural network (CNN) has shown great potential in recognizing different objects (e.g. human faces) from multiple slice (or color) images, a task similar to the polyp differentiation, given a large learning database. This study explores the potential of CNN learning from multiple slice (or feature

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Volumetric Textural Analysis of Colorectal Masses at CT Colonography

Abstract: VTA demonstrates excellent performance for distinguishing benign from malignant colorectal masses (≥3 cm) at CTC, comparable yet potentially complementary to experienced human performance.

Cited by 11 publications

References 33 publications

Appendiceal Inversion Presenting as a Cecal Polypoid Mass on Screening Colonoscopy: A Case Report and Review of Available Diagnostic Adjuncts to Differentiate Benign From Malignant Colorectal Pathology

Appendiceal Inversion Presenting as a Cecal Polypoid Mass on Screening Colonoscopy: A Case Report and Review of Available Diagnostic Adjuncts to Differentiate Benign From Malignant Colorectal Pathology

A dynamic lesion model for differentiation of malignant and benign pathologies

3D-GLCM CNN: A 3-Dimensional Gray-Level Co-Occurrence Matrix-Based CNN Model for Polyp Classification via CT Colonography

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