Table 3 Studies investigating the use of MRI texture analysis in diagnosis, treatment response assessment, and as a prognostic tool
From: Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?
Diagnosis and characterization | Method | Study findings | Author, year |
|---|---|---|---|
Diagnosis | |||
Breast | |||
Simulated microcalcification | Texture analysis | Successful automatic detection of localized blurring was achieved (sensitivity = 89 %–94 %; specificity = 99.7 %―100 %; PPV = 74 %–100 %; NPV = 99.9 %–99.9 %) | James et al., 2001 [45] |
Breast cancer | Texture analysis | A combination of textural analysis (second-order statistics, e.g., contrast, sum entropy, entropy), lesion size, time to maximum enhancement, and patient age allowed for a diagnostic accuracy of 0.92 ± 0.05 | Gibbs et al., 2003 [46] |
Breast lesion | Texture analysis | The classification performance of volumetric texture features (second-order statistics) is significantly better than 2D analysis | Chen et al., 2007 [47] |
Breast cancer | Texture analysis | The 4D texture analysis (using second-order statistics) achieved a performance comparable to human observers | Woods et al.,2007 [48] |
Invasive lobular and ductal breast cancer | Texture analysis | Investigated the use of first-order statistics, second-order stastistics obtained from GLCM, RLM, autoregressive model, and wavelet transform. All parameters distinguished healthy from cancerous tissue although GLCM performed better. 80 %–100 % of accuracy in differentiating ductal from lobular cancers, particularly complexity and entropy | Holli et al., 2010 [22] |
Brain | |||
Glioneuronal tumor | Texture analysis | The combination of DCE-MRI and MRI textural analysis (second-order statistics—GLCM and RLM) provide optimal differentiation between glioneuronal tumors and gliomas in vivo | Eliat et al., 2012 [19] |
Brain tumors—metastases, meningiomas, gliomas (grade II and III), glioblastomas | Texture analysis | Metastases were successfully distinguished from gliomas (accuracy = 85 %; sensitivity = 87 %; specificity = 79 %) as well as high-grade from low-grade neoplasms (accuracy = 88 %; sensitivity = 85 %; specificity = 96 %) using Gabor transform texture analysis | Zacharaki et al., 2009 [23] |
Prostate | |||
Prostate cancer | Fractal analysis | The combination of fractal and multifractal features was more accurate than classical texture features in detecting cancer and was more robust against signal intensity variations | Lopes et al., 2011 [21] |
Prostate cancer | Fractal analysis | Both fractal analyses offered promising quantitative indices for prostate cancer identification, with histogram fractal dimension offering a more robust diagnosis than texture fractal analysis (correlation coefficient of c = 0.9905 vs. c = 0.9458, respectively) | Lv et al., 2009 [49] |
Liver | |||
Liver cysts and hemangiomas | Texture analysis | Texture analysis (first-order, second-order statistics and wavelet transform) was successfully used to classify focal liver lesions on zero-fill interpolated 3.0-T MR images | Mayerhoefer et al., 2010 [20] |
Response assessment | |||
Breast | Texture analysis | Second-order statistics extracted from parametric maps that reflect lesion washout properties discriminate malignant from benign tumors better than textural features extracted from either first post-contrast frame lesion area or from parametric map reflecting lesion initial uptake. Angular second moment and entropy were most discriminative | Karahaliou et al., 2010 [26] |
Lymphoma | Texture analysis | Texture analysis [first-order, second-order statistics (GLCM and RLM), autoregressive model and wavelet transform] was able to classify NHL lesions undergoing chemotherapy based on changes following treatment | Harrison et al.,2009 [27] |
Liver metastases | Fractal analysis | Tumor heterogeneity as assessed by fractal dimension predicted tumor shrinkage in response to bevacizumab and cytotoxic chemotherapy in colorectal liver metastases | O’Connor et al., 2011 [28] |