Literature DB >> 25905936

Diagnosis of Sarcomatoid Renal Cell Carcinoma With CT: Evaluation by Qualitative Imaging Features and Texture Analysis.

Nicola Schieda1, Rebecca E Thornhill, Maali Al-Subhi, Matthew D F McInnes, Wael M Shabana, Christian B van der Pol, Trevor A Flood.   

Abstract

OBJECTIVE: The objective of our study was to determine whether CT findings, including texture analysis, can differentiate sarcomatoid renal cell carcinoma (RCC) from clear cell RCC.
MATERIALS AND METHODS: A retrospective case-control study was performed of consecutive patients with a histologic diagnosis of sarcomatoid RCC (n = 20) and clear cell RCC (n = 25) who underwent preoperative CT over a 3-year period. The CT images were independently reviewed by two blinded abdominal radiologists; they evaluated the following: tumor heterogeneity, tumor margin, calcification, intratumoral neovascularity, peritumoral neovascularity, renal sinus invasion, renal vein invasion, and adjacent organ invasion. Interobserver agreement was assessed using the Cohen kappa coefficient, and results were compared between groups using an independent Student t test and the chi-square test with a Bonferroni correction. For texture analysis, gray-level co-occurrence and run-length matrix features were extracted and compared using Mann-Whitney U tests. ROC curves for each tumor were constructed, and AUCs were calculated.
RESULTS: Overall, sarcomatoid RCCs were larger than clear cell RCCs, measuring 77 ± 27 mm (mean ± SD) compared with 50 ± 29 mm (p = 0.003), respectively; however, there was no difference in tumor size between the tumors that were compared using texture analysis or subjective analysis (p = 0.06 and 0.03, respectively). From the subjective analysis, only peritumoral neovascularity (readers 1 and 2: 70% and 70% sarcomatoid RCCs vs 0% and 41.6% clear cell RCCs, respectively; p = 0.001) and the size of the peritumoral vessels (p < 0.001) differed between sarcomatoid RCCs and clear cell RCCs, and interobserver agreement was fair (κ = 0.38). Other subjective imaging features did not differ between the tumors (p > 0.005). There was greater run-length nonuniformity and greater gray-level nonuniformity in sarcomatoid RCCs than in clear cell RCCs (p = 0.03 and p = 0.04, respectively). The combined textural features identified sarcomatoid RCC with an AUC of 0.81 ± 0.08 (standard error) (p < 0.0001).
CONCLUSION: Large tumor size, the presence of peritumoral neovascularity, and larger peritumoral vessels are features that are more commonly associated with sarcomatoid RCCs than with clear cell RCCs. Sarcomatoid RCCs are also more heterogeneous by texture analysis than clear cell RCCs.

Entities:  

Keywords:  CT; clear cell; renal cell carcinoma; sarcomatoid; texture analysis

Mesh:

Substances:

Year:  2015        PMID: 25905936     DOI: 10.2214/AJR.14.13279

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  30 in total

1.  Quantitative Assessment of Variation in CT Parameters on Texture Features: Pilot Study Using a Nonanatomic Phantom.

Authors:  K Buch; B Li; M M Qureshi; H Kuno; S W Anderson; O Sakai
Journal:  AJNR Am J Neuroradiol       Date:  2017-03-24       Impact factor: 3.825

2.  CT texture analysis of pancreatic cancer.

Authors:  Kumar Sandrasegaran; Yuning Lin; Michael Asare-Sawiri; Tai Taiyini; Mark Tann
Journal:  Eur Radiol       Date:  2018-08-16       Impact factor: 5.315

3.  CT texture analysis in the differentiation of major renal cell carcinoma subtypes and correlation with Fuhrman grade.

Authors:  Yu Deng; Erik Soule; Aster Samuel; Sakhi Shah; Enming Cui; Michael Asare-Sawiri; Chandru Sundaram; Chandana Lall; Kumaresan Sandrasegaran
Journal:  Eur Radiol       Date:  2019-05-24       Impact factor: 5.315

4.  Anorexia Nervosa: Analysis of Trabecular Texture with CT.

Authors:  Azadeh Tabari; Martin Torriani; Karen K Miller; Anne Klibanski; Mannudeep K Kalra; Miriam A Bredella
Journal:  Radiology       Date:  2016-10-31       Impact factor: 11.105

5.  [A radiomic approach to differential diagnosis of renal cell carcinoma in patients with hydronephrosis and renal calculi].

Authors:  Hang Zhang; Qing Li; Shulong Li; Jianhua Ma; Jing Huang
Journal:  Nan Fang Yi Ke Da Xue Xue Bao       Date:  2019-05-30

Review 6.  Radiomics in Kidney Cancer: MR Imaging.

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7.  Role of quantitative computed tomography texture analysis in the differentiation of primary lung cancer and granulomatous nodules.

Authors:  Carole Dennie; Rebecca Thornhill; Vineeta Sethi-Virmani; Carolina A Souza; Hamid Bayanati; Ashish Gupta; Donna Maziak
Journal:  Quant Imaging Med Surg       Date:  2016-02

8.  Stratification of cystic renal masses into benign and potentially malignant: applying machine learning to the bosniak classification.

Authors:  Nityanand Miskin; Lei Qin; Shanna A Matalon; Sree H Tirumani; Francesco Alessandrino; Stuart G Silverman; Atul B Shinagare
Journal:  Abdom Radiol (NY)       Date:  2020-07-01

9.  Role of Virtual Biopsy in the Management of Renal Masses.

Authors:  Alberto Diaz de Leon; Matthew S Davenport; Stuart G Silverman; Nicola Schieda; Jeffrey A Cadeddu; Ivan Pedrosa
Journal:  AJR Am J Roentgenol       Date:  2019-04-17       Impact factor: 3.959

10.  Texture analysis and machine learning algorithms accurately predict histologic grade in small (< 4 cm) clear cell renal cell carcinomas: a pilot study.

Authors:  Shawn Haji-Momenian; Zixian Lin; Bhumi Patel; Nicole Law; Adam Michalak; Anishsanjay Nayak; James Earls; Murray Loew
Journal:  Abdom Radiol (NY)       Date:  2020-03
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