Diagnosis of transition zone prostate cancer using T2-weighted (T2W) MRI: comparison of subjective features and quantitative shape analysis

Krishna, Satheesh; Schieda, Nicola; McInnes, Matthew D. F.; Flood, Trevor A.; Thornhill, Rebecca E.

doi:10.1007/s00330-018-5664-z

Cited by 19 publications

(23 citation statements)

References 42 publications

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“…81.8% (36/44) TZ tumors were identified in 36 patients and the sample was one of convenience. 22.7% (10/44) of the TZ cancers have been previously investigated by our group with mp‐MRI for diagnosis of extraprostatic extension and by using quantitative shape analysis; however, quantitative ADC, texture analysis, and logistic regression (LR) and support vector machine (SVM) modeling pertaining to the current study have not been previously studied or published by our group.…”

Section: Methodsmentioning

confidence: 99%

“…The criteria for selection of a BPH nodule have been described previously, but consisted mainly of a localized lesion/mass in the TZ corresponding to an area of BPH on histopathology. Since the vast majority of BPH nodules show mixed glandular and stromal features at histopathology, in this study, a stromal BPH nodule was diagnosed when it exhibited >90% iso‐to‐hypointense signal on T 2 W compared with the anterior fibromuscular stroma and a glandular BPH nodule was required to show >90% iso‐to‐hyperintensity on T 2 W compared with the adjacent normal PZ, as described previously . We selected 61 BPH nodules as the control group a priori to roughly approximate the number of TZ cancers in our study.…”

Section: Methodsmentioning

confidence: 99%

“…For T 2 W‐shape features, "circularity" and "convexity" were studied. These two quantitative features were selected because they had been previously investigated by Krishna et al and shown to be accurate in differentiating TZ PCa from BPH . Lastly, for T 2 W texture analysis, first‐order texture features "skewness," "kurtosis," and "entropy" and the second‐order texture features "RLNU" (run‐length nonuniformity) and "GLNU" (gray‐level nonuniformity) were assessed .…”

Section: Methodsmentioning

confidence: 99%

“…Quantitative texture analysis may also differentiate PCa from BPH and could aid the diagnosis of more aggressive disease . Most recently, Krishna et al demonstrated that quantitative shape analysis can differentiate TZ PCa from BPH . The purpose of this study was to evaluate previously described quantitative ADC, T 2 W‐shape, and T 2 W‐texture analysis features to differentiate TZ PCa from BPH.…”

mentioning

confidence: 99%

“…[22][23][24] Most recently, Krishna et al demonstrated that quantitative shape analysis can differentiate TZ PCa from BPH. 25 The purpose of this study was to evaluate previously described quantitative ADC, T 2 W-shape, and T 2 W-texture analysis features to differentiate TZ PCa from BPH.…”

mentioning

confidence: 99%

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Transition zone prostate cancer: Logistic regression and machine‐learning models of quantitative ADC, shape and texture features are highly accurate for diagnosis

Krishna

Thornhill

et al. 2019

Magnetic Resonance Imaging

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Background The limitation of diagnosis of transition zone (TZ) prostate cancer (PCa) using subjective assessment of multiparametric (mp) MRI with PI‐RADS v2 is related to overlapping features between cancers and stromal benign prostatic hyperplasia (BPH) nodules, particularly in small lesions. Purpose To evaluate modeling of quantitative apparent diffusion coefficient (ADC), texture, and shape features using logistic regression (LR) and support vector machine (SVM) models for the diagnosis of transition zone PCa. Study Type Retrospective. Population Ninety patients; 44 consecutive TZ PCa were compared with 61 consecutive BPH nodules (26 glandular/35 stromal). Field Strength/Sequence 3 T/T2‐weighted (T2W) fast spin‐echo, diffusion weighted imaging. Assessment A radiologist manually segmented lesions on axial images for quantitative ADC (mean, 10th, 25th‐centile‐ADC), T2W‐shape (circularity, convexity) and T2W‐texture (kurtosis, skewness, entropy, run‐length nonuniformity [RLNU], gray‐level nonuniformity [GLNU]) analysis. A second radiologist segmented one‐fifth of randomly selected lesions to determine the reproducibility of measurements. The reference standard was histopathology for all lesions. Statistical Tests Quantitative features were selected a priori and were compared using univariate and multivariate analysis. LR and SVM models of statistically significant features were constructed and evaluated using receiver operator characteristic (ROC) analysis. Subgroup analysis of TZ PCa vs. only stromal BPH and in lesions measuring <15 mm was performed. Agreement in measurements was assessed using the Dice similarity coefficient (DSC). Results Mean, 25th and 10th‐centile ADC, circularity, and texture (entropy, RLNU, GLNU) features differed between groups (P < 0.0001–0.0058); however, at multivariate analysis only circularity and ADC metrics (P < 0.001) remained significant. LR and SVM models were highly accurate for the diagnosis of TZ PCa (sensitivity/specificity/AUC): 93.2%/98.4%/0.989 and 93.2%/96.7%/0.949, respectively, with no significance difference between the LR and SVM models (P = 0.2271). Reproducibility of segmentation was excellent (DSC 0.84 tumors and 0.87 BPH). Subgroup analyses of TZ PCa vs. stromal BPH (AUC = 0.976) and in <15 mm lesions (AUC = 0.990) remained highly accurate. Data Conclusion LR and SVM models incorporating previously described quantitative ADC, shape and texture analysis features are highly accurate for the diagnosis of TZ PCa and remained accurate when comparing TZ PCa with stromal BPH and in smaller lesions. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:940–950.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Transition zone prostate cancer: Logistic regression and machine‐learning models of quantitative ADC, shape and texture features are highly accurate for diagnosis

Krishna

Thornhill

et al. 2019

Magnetic Resonance Imaging

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Introduction to matrix‐based method for analyzing hybrid multidimensional prostate MRI data

Fan,

Chatterjee,

Medved

et al. 2024

J Applied Clin Med Phys

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A new approach to analysis of prostate hybrid multidimensional MRI (HM‐MRI) data was introduced in this study. HM‐MRI data were acquired for a combination of a few echo times (TEs) and a few b‐values. Naturally, there is a matrix associated with HM‐MRI data for each image pixel. To process the data, we first linearized HM‐MRI data by taking the natural logarithm of the imaging signal intensity. Subsequently, a hybrid symmetric matrix was constructed by multiplying the matrix for each pixel by its own transpose. The eigenvalues for each pixel could then be calculated from the hybrid symmetric matrix. In order to compare eigenvalues between patients, three b‐values and three TEs were used, because this was smallest number of b‐values and TEs among all patients. The results of eigenvalues were displayed as qualitative color maps for easier visualization. For quantitative analysis, the ratio (λr) of eigenvalues (λ1, λ2, λ3) was defined as λr = (λ1/λ2)/λ3 to compare region of interest (ROI) between prostate cancer (PCa) and normal tissue. The results show that the combined eigenvalue maps show PCas clearly and these maps are quite different from apparent diffusion coefficient (ADC) and T2 maps of the same prostate. The PCa has significant larger λr, smaller ADC and smaller T2 values than normal prostate tissue (p < 0.001). This suggests that the matrix‐based method for analyzing HM‐MRI data provides new information that may be clinically useful. The method is easy to use and could be easily implemented in clinical practice. The eigenvalues are associated with combination of ADC and T2 values, and could aid in the identification and staging of PCa.

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Diagnosis of transition zone prostate cancer using T2-weighted (T2W) MRI: comparison of subjective features and quantitative shape analysis

Abstract: • Presence of a complete T2-weighted hypointense circumscribed rim accurately diagnoses BPH. • Round shape accurately diagnoses BPH and can be assessed quantitatively using circularity. • Lenticular shape accurately diagnoses TZ PCa and can be assessed quantitatively using convexity.

Cited by 19 publications

References 42 publications

Transition zone prostate cancer: Logistic regression and machine‐learning models of quantitative ADC, shape and texture features are highly accurate for diagnosis

Transition zone prostate cancer: Logistic regression and machine‐learning models of quantitative ADC, shape and texture features are highly accurate for diagnosis

Introduction to matrix‐based method for analyzing hybrid multidimensional prostate MRI data

Interobserver Agreement of PI‐RADS v. 2: Not All Features or Observers Are Created Equal

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