Tumors are rarely biologically uniform. Competing malignant clones typically inhabit different vascular and oxygen microenvironments throughout a tumor’s volume, and they frequently harbor distinct genetic mutations, metabolisms, and cellular phenotypes. This tumor heterogeneity can be an important correlate of grade, metastatic potential, treatment efficacy, durability of treatment response, and ultimately, patient survival. Identifying biomarkers of tumor heterogeneity is an important goal among researchers seeking to improve diagnostic and prognostic accuracy.

Computed tomography tumor texture analysis (CTTA) is a promising image analysis technique for cancer diagnosis and outcome prediction. CTTA quantifies and maps CT pixel grayscale value (tissue density) heterogeneity based on pixel values (intensity) throughout the tumor, and on the spatial relationships between neighboring pixels.1-13

The CT grayscale intensity distributions in tumors or surrounding, nontarget tissues are summarized with histograms and statistical measures such as average, standard deviation (variation in values), entropy (heterogeneity), and skewness and kurtosis (measures of the distribution of grayscale values, or a histogram’s shape and symmetry).12 These statistical measures can then be used to calculate texture “signatures” that serve as diagnostic, predictive, and prognostic biomarkers.1-4

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The field is young and investigational, but findings thus far indicate that CTTA can help to clarify ambiguous traditional anatomic medical images, particularly when tumors are small, and to differentiate nonmalignant cysts and conditions from malignant tumors, even differentiate between tumor subtypes. This could help patients and clinicians decide whether to proceed with additional testing or treatment.5-12

CTTA signatures are under investigation as tools for monitoring tumor responses to treatment. Tracking tumor texture over time can be used to assess whether radiotherapy or other treatment is working and how a tumor is evolving in response to treatment, helping clinicians determine if the treatment is working or to consider revising a patient’s treatment plan.13-15 CTTA may also identify nontumor tissues and organs at risk for radiotoxicities such as xerostomia and radiation-induced pneumonitis, informing radiation oncology treatment planning, and patient education, monitoring, and management.13-16

Potential to Improve Kidney Cancer Management

CTTA research is underway for a number of tumor types. Several recent studies have spotlighted the potential contributions of CTTA to diagnostic and predictive work-ups for patients with kidney cancer.2,6,7,11,17,18 For example, CTTA entropy (heterogeneity) is higher in images of renal cell carcinoma (RCC) than benign renal tumors7 and, according to a 2021 meta-analysis of findings from 11 studies, between low- and high-grade RCC tumors.17 With machine learning, CTTA can also distinguish between clear cell and non-clear cell RCCs — but not between clear cell, papillary cell, and chromophobe cell RCC tumors specifically.6 CTTA heterogeneity can also predict before surgery the grade of clear cell RCCs, according to a Chinese study of 131 cases.11

CTTA also shows early promise in clarifying which patients might benefit from specific treatment strategies. 

“The diverse treatment outcomes of patients with metastatic renal cell carcinoma (mRCC) following immune checkpoint inhibitor (ICI)-based therapies necessitate reliable prognostic biomarkers,” a research team from South Korea and the Dana-Farber Cancer Institute and Brigham and Women’s Hospital in Boston noted last year.2

CTTA offers a noninvasive biomarker for tumor correlates of survival, so it might be a useful prognostic tool, they reasoned. To find out, they conducted a small retrospective analysis of CTTA texture findings at baseline (before treatment) and within 3 months of treatment for 68 patients with mRCC receiving programmed death receptor-1 (PD-1)/programmed death ligand-1 (PD-L1) inhibitors — eg, nivolumab, atezolizumab — between 2012 and 2019.2

The team found that baseline tumor texture can differentiate longer-term from shorter-term survivors using both progression-free survival (PFS; median, 5.2 vs 2.8 months) and overall survival (OS; median, 40.3 vs 15.2 months) outcomes; follow-up image texture analysis did so for OS (40.3 vs 15.2 months; P =.008) but not PFS.2 A statistical model combining CTTA and clinical factors (age, sex, tumor histology, and IMDC risk category) outperformed clinical factors alone in predicting both OS and PFS, they reported.2

“The present study demonstrates that texture analysis using CT obtained before and early after ICI-based treatment may help in predicting treatment outcomes in patients with mRCC,” the coauthors concluded.2

The Future of CTTA Imaging

CTTA is still a relatively young field of imaging and texture analyses can be affected by the quality of a CT scan, such as noise or blurring, or metal streaking associated with dental fillings in head and neck imaging, for example.15 This has prompted calls for standardized imaging protocols for CTTA, as well as standardizing the study methods used to assess its accuracy, and to identify its correlates and uses.8,15,17 Other significant steps needed for the widespread clinical adoption of CTTA include standardizing the texture signature calculations across software platforms, and integrating CTTA into radiologists’ workflows.19,20

But as that happens and this technology matures, it is poised to become an important tool in clinical oncology, most likely as an addition to traditional clinical factors in diagnostic and predictive imaging and pathology rather than a replacement for them. Clinicians who are familiar with this important emerging tool will be better positioned to help patients make informed treatment decisions. Ensuring patients understand their CTTA results and what it means for their diagnosis, treatment, and prognosis will fall largely to oncology nurses.

But to get there, research is needed using larger and more diverse datasets, and more must be learned about the biological basis and variability in tumor and nontumor tissue texture. Standardization of CT scanning parameters and CTTA software algorithms are also needed to reliably compare results from different studies and to develop widely accepted clinical guidelines.


1. Liu S, Shi H, Ji C, et al. CT textural analysis of gastric cancer: correlations with immunohistochemical biomarkers. Sci Rep. 2018;8:11844. doi:10.1038/s41598-018-30352-6

2. Park HJ, Qin L, Bakouny Z, et al. Computed tomography texture analysis for predicting clinical outcomes in patients with metastatic renal cell carcinoma treated with immune checkpoint inhibitors. Oncologist. 2022;27(5):389-397. doi:10.1093/oncolo/oyac034

3. Alessandrino F, Gujrathi R, Nassar AH, et al. Predictive role of computed tomography texture analysis in patients with metastatic urothelial cancer treated with programmed death-1 and programmed death-ligand 1 inhibitors. Eur Urol Oncol. 2020;3(5):680-686. doi:10.1016/j.euo.2019.02.002

4. Ganeshan B, Skogen K, Pressney I, Coutroubis D, Miles K. Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. Clin Radiol. 2012;67(2):157-164. doi:10.1016/j.crad.2011.08.012

5. Yasaka K, Akai H, Abe O, Ohtomo K, Kiryu S. Quantitative computed tomography texture analysis for anterior mediastinal masses: differentiation between solid masses and cysts. Eur J Radiol. 2018;100:85-91. doi:10.1016/j.ejrad.2018.01.017

6. Kocak B, Yardimci AH, Bektas CT, et al. Textural differences between renal cell carcinoma subtypes: machine learning-based quantitative computed tomography texture analysis with independent external validation. Eur J Radiol. 2018;107:149-157. doi:10.1016/j.ejrad.2018.08.014

7. Deng Y, Soule E, Cui E, et al. Usefulness of CT texture analysis in differentiating benign and malignant renal tumours. Clin Radiol. 2020;75(2):108-115. doi:10.1016/j.crad.2019.09.131

8. Adelsmayr G, Janisch M, Müller H, et al. Three dimensional computed tomography texture analysis of pulmonary lesions: does radiomics allow differentiation between carcinoma, neuroendocrine tumor and organizing pneumonia? Eur J Radiol. 2023;165:110931. doi:10.1016/j.ejrad.2023.110931

9. Wang JY, Sun D, Liu CY, et al. Differentiation of giant cell tumours of bone, primary aneurysmal bone cysts, and aneurysmal bone cysts secondary to giant cell tumour of bone: using whole-tumour CT texture analysis parameters as quantitative biomarkers. Clin Radiol. 2023;78(7):532-539. doi:10.1016/j.crad.2023.03.004

10. Meyer HJ, Hamerla G, Höhn AK, Surov A. CT texture analysis — correlations with histopathology parameters in head and next squamous cell carcinomas. Front Oncol. 2019;9:00444. doi:10.3389/fonc.2019.00444

11. Feng Z, Shen Q, Li Y, Hu Z. CT texture analysis: a potential tool for predicting the Fuhrman grade of clear-cell renal carcinoma. Cancer Imaging. 2019;19(1):6. doi:10.1186/s40644-019-0195-7

12. Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. CT texture analysis: definitions, applications, biological correlates, and challenges. Radiographics. 2017;37(5):1483-1503. doi:10.1148/rg.2017170056

13. Stieb S, Kiser K, van Dijk L, et al. Imaging for response assessment in radiation oncology: current and emerging techniques. Hematol Oncol Clin North Am. 2020;34(1):293-306. doi:10.1016/j.hoc.2019.09.010

14.  Ger RB, Wei L, El Naqa I, Wang J. The promise and future of radiomics for personalized radiotherapy dosing and adaptation. Semin Radiat Oncol. 2023;33(3):252-261. doi:10.1016/j.semradonc.2023.03.003

15. Scalco E, Rizzo G. Texture analysis of medical images for radiotherapy applications. Br J Radiol. 2017;90(1070):20160642. doi:10.1259/bjr.20160642

16. Scarborough JA, Scott JG. Translation of precision medicine research into biomarker-informed care in radiation oncology. Semin Radiat Oncol. 2021;32:42-53. doi:10.1016/j.semradonc.2021.09.001

17. Yu W, Liang G, Zeng L, Yang Y, Wu Y. Accuracy of CT texture analysis for differentiating low-grade and high-grade renal cell carcinoma: systematic review and meta-analysis. BMJ Open. 2021;11:e051470. doi:10.1136/bmjopen-2021-051470

18. Marigliano C, Badia S, Bellini D, et al. Radiogenomics in clear cell renal cell carcinoma: correlations between advanced CT imaging (texture analysis) and microRNAs expression. Technol Cancer Res Treat. 2019;18:1533033819878458. doi:10.1177/1533033819878458

19. Miles KA, Squires J, Murphy M. Radiologist engagement as a potential barrier to the clinical translation of quantitative imaging for the assessment of tumor heterogeneity. Acad Radiol. 2018;25(7):935-942. doi:10.1016/j.acra.2017.11.019

20. Dreyfuss LD, Abel EJ, Nystrom J, Stabo NJ, Pickhardt PJ, Lubner MG. Comparison of CT texture analysis software platforms in renal cell carcinoma: reproducibility of numerical values and association with histologic subtype across platforms. AJR Am J Roentgenol. 2021;216(6):1549-1557. doi:10.2214/AJR/20/22823