UROGENITAL RADIOLOGY / ORIGINAL PAPER
The use of ADC histogram analysis in the diagnosis and determination of aggressiveness of peripheral zone prostate cancer
1
University of Health Sciences, Beyhekim Training and Research Hospital, Konya, Türkiye
2
University of Health Sciences, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, Türkiye
Submission date: 2025-03-13
Final revision date: 2025-05-21
Acceptance date: 2025-05-22
Publication date: 2025-07-28
Corresponding author
Halil İbrahim Şara
University of Health Sciences, Beyhekim Training and Research Hospital, Department of Radiology, Konya, Türkiye
University of Health Sciences, Beyhekim Training and Research Hospital, Department of Radiology, Konya, Türkiye
Pol J Radiol, 2025; 90: 374-383
KEYWORDS
TOPICS
ABSTRACT
Purpose:
The purpose of this study was to determine the effectiveness of ADC histogram analysis in diagnosing and determining the aggressiveness of peripheral zone (PZ) prostate cancer, and to reveal the relationship between Gleason and PI-RADS scores.
Material and methods:
61 patients who underwent standard 12-core and cognitive prostate biopsy and multiparametric prostate magnetic resonance imaging before biopsy were included in the study. According to the pathology results, patients were classified as either having clinically significant cancer with malignancy (n = 35) or as clinically insignificant – benign (n = 26). The effectiveness of ADC histogram parameters to distinguish between benign and malignant lesions was investigated. Subsequently, 35 patients in the malignant group were grouped according to their Gleason scores, and the relationship between ADC histogram parameters and Gleason scores was examined.
Results:
ADC max, standard deviation, entropy, voxel count, and volume were found to be significantly different between the benign and malignant groups (p < 0.05; p < 0.05; p < 0.01; p < 0.01; p < 0.01). According to the ROC curve: entropy (AUC = 0.75; 95% CI: 0.63-0.87), voxel count (AUC = 0.83; 95% CI: 0.73-0.93), and volume values (AUC = 0.83; 95% CI: 0.73-0.93) were statistically significant in the diagnosis of benign and malignant lesions in the prostate gland (area under the ROC curves). In the logistic regression analysis models (backward), it was found that an increase in volume increased the risk of malignant tumours by 1.75 times (p = 0.04; OR = 1.75; 95% CI: 1.00-3.04).
Conclusions:
ADC histogram data contribute to the diagnosis of benign-malignant differentiation in PZ prostate lesions and predict the Gleason score in malignant lesions.
The purpose of this study was to determine the effectiveness of ADC histogram analysis in diagnosing and determining the aggressiveness of peripheral zone (PZ) prostate cancer, and to reveal the relationship between Gleason and PI-RADS scores.
Material and methods:
61 patients who underwent standard 12-core and cognitive prostate biopsy and multiparametric prostate magnetic resonance imaging before biopsy were included in the study. According to the pathology results, patients were classified as either having clinically significant cancer with malignancy (n = 35) or as clinically insignificant – benign (n = 26). The effectiveness of ADC histogram parameters to distinguish between benign and malignant lesions was investigated. Subsequently, 35 patients in the malignant group were grouped according to their Gleason scores, and the relationship between ADC histogram parameters and Gleason scores was examined.
Results:
ADC max, standard deviation, entropy, voxel count, and volume were found to be significantly different between the benign and malignant groups (p < 0.05; p < 0.05; p < 0.01; p < 0.01; p < 0.01). According to the ROC curve: entropy (AUC = 0.75; 95% CI: 0.63-0.87), voxel count (AUC = 0.83; 95% CI: 0.73-0.93), and volume values (AUC = 0.83; 95% CI: 0.73-0.93) were statistically significant in the diagnosis of benign and malignant lesions in the prostate gland (area under the ROC curves). In the logistic regression analysis models (backward), it was found that an increase in volume increased the risk of malignant tumours by 1.75 times (p = 0.04; OR = 1.75; 95% CI: 1.00-3.04).
Conclusions:
ADC histogram data contribute to the diagnosis of benign-malignant differentiation in PZ prostate lesions and predict the Gleason score in malignant lesions.
REFERENCES (36)
2.
Barentsz JO, Richenberg J, Clements R, Choyke P, Verma S, Villeirs G, et al. ESUR prostate MR guidelines 2012. Eur Radiol 2012; 22: 746-757.
3.
Turkbey B, Rosenkrantz AB, Haider MA, Padhani AR, Villeirs G, Macura KJ, et al. Prostate Imaging Reporting and Data System version 2.1: 2019 update of Prostate Imaging Reporting and Data System version 2. Eur Urol 2019; 76: 340-351.
4.
Castellano G, Bonilha L, Li LM, Cendes F. Texture analysis of medical images. Clin Radiol 2004; 59: 1061-1069.
5.
Somwanshi DK, Yadav AK, Roy R. Medical images texture analysis: a review. In: 2017 International Conference on Computer, Communications and Electronics (Comptelix). Jaipur, India, 2017, pp. 436-441. DOI: 10.1109/COMPTELIX.2017.8004009.
6.
Antunovic L, De Sanctis R, Cozzi L, Kirienko M, Sagona A, Torrisi R, et al. PET/CT radiomics in breast cancer: promising tool for prediction of pathological response to neoadjuvant chemotherapy. Eur J Nucl Med Mol Imaging 2019; 46: 1468-1477.
7.
Ba-Ssalamah A, Muin D, Schernthaner R, Kulinna-Cosentini C, Bastati N, Stift J, et al. Texture-based classification of different gastric tumors at contrast-enhanced CT. Eur J Radiol 2013; 82: e537-e543. DOI: 10.1016/j.ejrad.2013.06.024.
8.
Donati OF, Mazaheri Y, Afaq A, Vargas HA, Zheng J, Moskowitz CS, et al. Prostate cancer aggressiveness: assessment with whole-lesion histogram analysis of the apparent diffusion coefficient. Radiology 2014; 271: 143-152.
9.
Liu HL, Zong M, Wei H, Wang C, Lou JJ, Wang SQ, et al. Added value of histogram analysis of apparent diffusion coefficient maps for differentiating triple-negative breast cancer from other subtypes of breast cancer on standard MRI. Cancer Manag Res 2019; 11: 8239-8247.
10.
Westphalen AC, McCulloch CE, Anaokar JM, Arora S, Barashi NS, Barentsz JO, et al. Variability of the positive predictive value of PI-RADS for prostate MRI across 26 centers: experience of the society of abdominal radiology prostate cancer disease-focused panel. Radiology 2020; 296: 76-84.
11.
Bittencourt LK, Attenberger UI, Lima D, Strecker R, de Oliveira A, Schoenberg SO, et al. Feasibility study of computed vs measured high b-value (1400 s/mm²) diffusion-weighted MR images of the prostate. World J Radiol 2014; 6: 374-380.
12.
Turkbey B, Mani H, Aras O, Rastinehad AR, Shah V, Bernardo M, et al. Correlation of magnetic resonance imaging tumor volume with histopathology. J Urol 2012; 188: 1157-1163.
13.
Fleiss JL, Levin B, Paik MC. Statistical Methods for Rates and Proportions. John Wiley & Sons; 2013.
14.
Richard R, Thomassin I, Chapellier M, Scemama A, de Cremoux P, Varna M, et al. Diffusion-weighted MRI in pretreatment prediction of response to neoadjuvant chemotherapy in patients with breast cancer. Eur Radiol 2013; 23: 2420-2431.
15.
Cipolla V, Santucci D, Guerrieri D, Drudi FM, Meggiorini ML, de Felice C. Correlation between 3 T apparent diffusion coefficient values and grading of invasive breast carcinoma. Eur J Radiol 2014; 83: 2144-2150.
16.
Bougias H, Ghiatas A, Priovolos D, Veliou K, Christou A. Whole lesion histogram analysis metrics of the apparent diffusion coefficient as a marker of breast lesions characterization at 1.5 T. Radiography (Lond) 2017; 23: e41-e46. DOI: 10.1016/j.radi.2017.02.002.
17.
Tamada T, Huang C, Ream JM, Taffel M, Taneja SS, Rosenkrantz AB. Apparent diffusion coefficient values of prostate cancer: comparison of 2D and 3D ROIs. AJR Am J Roentgenol 2018; 210: 113-117.
18.
Ng F, Kozarski R, Ganeshan B, Goh V. Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis? Eur J Radiol 2013; 82: 342-348.
19.
Lin W, Westphalen AC, Silva GE, Chodraui Filho S, Reis RB, Mu-glia VF. Comparison of PI-RADS 2, ADC histogram-derived parameters, and their combination for the diagnosis of peripheral zone prostate cancer. Abdom Radiol (NY) 2016; 41: 2209-2217.
20.
Oto A, Yang C, Kayhan A, Tretiakova M, Antic T, Schmid-Tannwald C, et al. Diffusion-weighted and dynamic contrast-enhanced MRI of prostate cancer: correlation of quantitative MR parameters with Gleason score and tumor angiogenesis. AJR Am J Roentgenol 2011; 197: 1382-1390.
21.
Vargas HA, Akin O, Franiel T, Mazaheri Y, Zheng J, Moskowitz C, et al. Diffusion-weighted endorectal MR imaging at 3 T for prostate cancer: tumor detection and assessment of aggressiveness. Radiology 2011; 259: 775-784.
22.
Turkbey B, Shah VP, Pang Y, Bernardo M, Xu S, Kruecker J et al. Is apparent diffusion coefficient associated with clinical risk scores for prostate cancers that are visible on 3-T MR images? Radiology 2011; 258: 488-495.
23.
Peng Y, Jiang Y, Yang C, Brown JB, Antic T, Sethi I, et al. Quantitative analysis of multiparametric prostate MR images: differentiation between prostate cancer and normal tissue and correlation with Gleason score – a computer-aided diagnosis development study. Radiology 2013; 267: 787-796.
24.
Bao J, Wang X, Hu C, Hou J, Dong F, Guo L. Differentiation of prostate cancer lesions in the transition zone by diffusion-weighted MRI. Eur J Radiol Open 2017; 4: 123-128.
25.
Kumari K. Correlation between Gleason score of adenocarcinoma prostate and serum PSA levels in the western Himalayan region of India. Indian J Pathol Oncol 2020. DOI: https://doi.org/10.18231/J.IJP....
26.
Ngwu P, Achor GO, Eziefule VU, Orji JI, Alozie FT. Correlation between prostate specific antigen and prostate biopsy Gleason score. Ann Health Res 2019; 5: 243-248.
27.
Okolo CA, Akinosun OM, Shittu OB, Olapade-Olaopa EO, Okeke LI, Akang EEU, Ogunbiyi JO. Correlation of serum PSA and Gleason Score in Nigerian men with prostate cancer. Afr J Urol 2008; 14: 15-22.
28.
Gupta R, Mahajan M, Sharma P. Correlation between prostate imaging reporting and data system version 2, prostate-specific antigen levels, and local staging in biopsy-proven carcinoma prostate: a retrospective study. Int J Appl Basic Med Res 2021; 11: 32-35.
29.
Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics 2017; 37: 1483-1503.
30.
Raman SP, Chen Y, Schroeder JL, Huang P, Fishman EK. CT texture analysis of renal masses: pilot study using random forest classification for prediction of pathology. Acad Radiol 2014; 21: 1587-1596.
31.
Lubner MG, Stabo N, Abel EJ, Del Rio AM, Pickhardt PJ. CT textural analysis of large primary renal cell carcinomas: pretreatment tumor heterogeneity correlates with histologic findings and clinical outcomes. Am J Roentgenol 2016; 207: 96-105.
32.
Nketiah GA, Elschot M, Scheenen TW, Maas MC, Bathen TF, Selnæs KM; PCa-MAP Consortium. Utility of T2-weighted MRI texture analysis in assessment of peripheral zone prostate cancer aggressiveness: a single-arm, multicenter study. Sci Rep 2021; 11: 2085. DOI: 10.1038/s41598-021-81272-x.
33.
Rosenkrantz AB, Triolo MJ, Melamed J, Rusinek H, Taneja SS, Deng FM. Whole-lesion apparent diffusion coefficient metrics as a marker of percentage Gleason 4 component within Gleason 7 prostate cancer at radical prostatectomy. J Magn Res Imaging 2015; 41: 708-714.
34.
Andras I, Telecan T, Crisan D, Cata E, Kadula P, Andras D, et al. Different clinical significance of ASAP/HGPIN pattern in systematic vs. MRI US fusion guided prostate biopsy. Exp Ther Med 2020; 20: 195. DOI: 10.3892/etm.2020.9325.
35.
Puech P, Rouvière O, Renard-Penna R, Villers A, Devos P, Colombel M, et al. Prostate cancer diagnosis: multiparametric MR-targeted biopsy with cognitive and transrectal US-MR fusion guidance versus systematic biopsy—prospective multicenter study. Radiology 2013; 268: 461-469.
36.
Caruso D, Zerunian M, De Santis D, Biondi T, Paolantonio P, Rengo M, et al. Magnetic resonance of rectal cancer response to therapy: an image quality comparison between 3.0 and 1.5 Tesla. Biomed Res Int 2020; 2020: 9842732. DOI: 10.1155/2020/9842732.
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