Quantitative CT texture analysis for evaluating histologic grade of urothelial carcinoma

Zhang, Gumuyang; Sun, Hansong; Shi, Bing; Jin, Zhengyu; Xue, Huadan

doi:10.1007/s00261-016-0897-2

Cited by 55 publications

(29 citation statements)

References 29 publications

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“…A radiomics approach has the potential to act as a noninvasive tool to assist in preoperative grading of urothelial cancer. But the sample size of these studies is small, and as UTUC is quite rare compared to bladder cancer, only our group and Mammen et al have included UTUC [14,15]. More data should be gathered to further evaluate the ability of radiomics for predicting pathological grade, especially in UTUC.…”

Section: Evaluation Of Pathological Gradementioning

confidence: 99%

“…A few studies have explored the feasibility of texture analysis, machine learning and radiomics to distinguish between low-and high-grade urothelial cancer. A study by our group in 105 patients with urothelial carcinoma found that low-grade tumors demonstrated lower texture features of mean, entropy and mean of positive pixels (MPP) quantified from CT images and MPP could differentiate low-from high-grade tumors with an area under the curve (AUC) of 0.779 [14]. Mammen et al performed a similar texture analysis of CT scans of 48 patients with UTUC and found entropy was also greater in high-grade tumors with an AUC of 0.83 [15].…”

Section: Evaluation Of Pathological Gradementioning

confidence: 99%

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Current applications and challenges of radiomics in urothelial cancer

Zhang

Sun

et al. 2019

Chin J Acad Radiol

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New discoveries and technologies have begun to change paradigms of urothelial cancer therapy in recent years. One of the novel techniques which emerged in the imaging community is radiomics, which refers to the high-throughput extraction of quantitative image features from medical images. Radiomics, being noninvasive and easy to perform, has shown great potential in oncology by providing valuable information about tumor type, aggressiveness, progression, response to treatment and prognosis and enabling us to gain insights into the true utility of personalized medicine in the management of cancer in the near future. With rapid development in this area, radiomics has already been applied in urothelial cancer to predict pathological grade, clinical stage, lymph node metastasis and treatment response demonstrating promising results. In this review, we highlight advances in clinical applications of radiomics in urothelial cancer, discuss about the challenges and implications of radiomics for radiologists and suggest the future directions that we could move toward in order to fully realize the potentials of radiomics to improve personalized management of patients with urothelial cancer.

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Section: Evaluation Of Pathological Gradementioning

confidence: 99%

Section: Evaluation Of Pathological Gradementioning

confidence: 99%

Current applications and challenges of radiomics in urothelial cancer

Zhang

Sun

et al. 2019

Chin J Acad Radiol

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“…Zhang et al showed that CT texture analysis proved to be a feasible tool for differentiating low-grade UC from high-grade UC. 23 Further examinations in many cases are necessary to verify the utility of these methods and establish a preoperative prediction system.…”

Section: T2 or Lower Stages Versus T3 Or Higher Stagesmentioning

confidence: 99%

Clinical staging of upper urinary tract urothelial carcinoma for T staging: Review and pictorial essay

et al. 2019

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Upper urinary tract urothelial carcinoma is staged using the TNM classification of malignant tumors. Preoperative TNM is important for treatment planning. Computed tomography urography is now widely used for clinical survey of upper urinary tract carcinoma because of its diagnostic accuracy. Computed tomography urography is recommended as the first‐line imaging procedure in several guidelines. Several reports stated that computed tomography urography is also useful for staging. However, no educational and practical reviews detailing the T staging of upper urinary tract urothelial carcinomas using imaging are available. We discuss the scanning protocol, T staging using computed tomography urography, limitations, magnetic resonance imaging, computed tomography comparison and pitfalls in imaging of upper urinary tract urothelial carcinoma. A recent study reported the high diagnostic accuracy of computed tomography urography with respect to T3 or higher stage tumors. To date, images that show a Tis–T2 stage have not been reported, but various studies are ongoing. Although magnetic resonance imaging has lower spatial resolution than computed tomography urography, magnetic resonance imaging can be carried out without radiation exposure or contrast agents. Magnetic resonance imaging also offers the unique ability of diffusion‐weighted imaging without contrast agent use. Some researchers reported that diffusion‐weighted imaging is useful not only for detecting lesions, but for predicting the T stage and tumor grade. We recommend the appropriate use of computed tomography and magnetic resonance while considering the limitations of each modality and the pitfalls in upper urinary tract urothelial carcinoma imaging.

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“…Whether texture analysis is useful for diagnosis of >T2 disease remains unknown and requires further study. In addition to tumor stage, radiomic features have been found to provide information on tumor grade and disease aggressiveness, factors that impact both medical and surgical management . DCE‐derived tumor washout may be associated with higher‐grade cancer and quantitative ADC values have been shown to inversely correlate with tumor grade .…”

Section: Radiomic Analysismentioning

confidence: 99%

“…In addition to tumor stage, radiomic features have been found to provide information on tumor grade and disease aggressiveness, factors that impact both medical and surgical management. 93,[95][96][97] DCE-derived tumor washout may be associated with higher-grade cancer 93 and quantitative ADC values have been shown to inversely correlate with tumor grade. 92 Combining ADC values with different texture features (including entropy and kurtosis) shows promise for predicting higher-grade bladder cancer, although further work is needed to verify the results from these preliminary studies.…”

Section: Radiomic Analysismentioning

confidence: 99%