Multi-sequence texture analysis in classification of in vivo MR images of the prostate

MR Imaging Texture Analysis in the Abdomen and Pelvis

Add "which is a" before "distribution"? Texture analysis (TA) is a form of radiomics that refers to quantitative measurements of the histogram, distribution and/or relationship of pixel intensities or gray scales within a region of interest on an image. TA can be applied to MR images of the abdomen and pelvis, with the main strength quantitative analysis of pixel intensities and heterogeneity rather than subjective/qualitative analysis. There are multiple limitations of MRTA. Despite these limitations, there is a growing body of literature supporting MRTA. This review discusses application of MRTA to the abdomen and pelvis.

Prostate MRI radiomics: A systematic review and radiomic quality score assessment

BACKGROUND

Radiomics have the potential to further increase the value of MRI in prostate cancer management. However, implementation in clinical practice is still far and concerns have been raised regarding the methodological quality of radiomic studies. Therefore, we aimed to systematically review the literature to assess the quality of prostate MRI radiomic studies using the radiomics quality score (RQS).

METHODS

Multiple medical literature archives (PubMed, Web of Science and EMBASE) were searched to retrieve original investigations focused on prostate MRI radiomic approaches up to the end of June 2019. Three researchers independently assessed each paper using the RQS. Data from the most experienced researcher were used for descriptive analysis. Inter-rater reproducibility was assessed using the intraclass correlation coefficient (ICC) on the total RQS score.

RESULTS

73 studies were included in the analysis. Overall, the average RQS total score was 7.93 ± 5.13 on a maximum of 36 points, with a final average percentage of 23 ± 13%. Among the most critical items, the lack of feature robustness testing strategies and external validation datasets. The ICC resulted poor to moderate, with an average value of 0.57 and 95% Confidence Intervals between 0.44 and 0.69.

CONCLUSIONS

Current studies on prostate MRI radiomics still lack the quality required to allow their introduction in clinical practice.

Importance of Vertices in Complex Networks Applied to Texture Analysis

Texture analysis has attracted increasing attention in computer vision due to its power in describing images and the physical properties of objects. Among the methods for texture analysis, complex network (CN)-based ones have emerged to model images because of their flexibility. In image modeling, each pixel is mapped to a vertex of the CN and two vertices are connected if they are spatially close in the image. Then measurements are extracted from the CN to characterize its topology and therefore characterize the image content. Despite the promising results, the accuracy of these methods depends on the suitability of the measurement for the application. In texture analysis, simple measurements have been used, such as those based on vertex degree and shortest paths. Motivated by these issues, this paper proposes a new method for texture analysis based on the CN and a new measurement that calculates the importance of each vertex within its neighborhood. For calculating the importance of vertices, we extend the pagerank to CN in order to correlate the vertex importance with its degree and show that this new measurement extracts texture properties. Experimental results on well-known datasets and in the recognition of soybean diseases using leaf texture show the effectiveness of the proposed method for texture recognition.

Multiparametric MRI and radiomics in prostate cancer: a review

Multiparametric MRI (mpMRI) is an imaging modality that combines anatomical MR imaging with one or more functional MRI sequences. It has become a versatile tool for detecting and characterising prostate cancer (PCa). The traditional role of mpMRI was confined to PCa staging, but due to the advanced imaging techniques, its role has expanded to various stages in clinical practises including tumour detection, disease monitor during active surveillance and sequential imaging for patient follow-up. Meanwhile, with the growing speed of data generation and the increasing volume of imaging data, it is highly demanded to apply computerised methods to process mpMRI data and extract useful information. Hence quantitative analysis for imaging data using radiomics has become an emerging paradigm. The application of radiomics approaches in prostate cancer has not only enabled automatic localisation of the disease but also provided a non-invasive solution to assess tumour biology (e.g. aggressiveness and the presence of hypoxia). This article reviews mpMRI and its expanding role in PCa detection, staging and patient management. Following that, an overview of prostate radiomics will be provided, with a special focus on its current applications as well as its future directions.

Prostate Cancer Differentiation and Aggressiveness: Assessment With a Radiomic-Based Model vs. PI-RADS v2

Background

Multiparametric MRI (mp‐MRI) combined with machine‐aided approaches have shown high accuracy and sensitivity in prostate cancer (PCa) diagnosis. However, radiomics‐based analysis has not been thoroughly compared with Prostate Imaging and Reporting and Data System version 2 (PI‐RADS v2) scores.

Purpose

To develop and validate a radiomics‐based model for differentiating PCa and assessing its aggressiveness compared with PI‐RADS v2 scores.

Study Type

Retrospective.

Population

In all, 182 patients with biopsy‐proven PCa and 199 patients with a biopsy‐proven absence of cancer were enrolled in our study.

Field Strength/Sequence

Conventional and diffusion‐weighted MR images (b values = 0, 1000 sec/mm²) were acquired on a 3.0T MR scanner.

Assessment

A total of 396 features and 385 features were extracted from apparent diffusion coefficient (ADC) images and T₂WI, respectively. A predictive model was constructed for differentiating PCa from non‐PCa and high‐grade from low‐grade PCa. The diagnostic performance of each radiomics‐based model was compared with that of the PI‐RADS v2 scores.

Statistical Tests

A radiomics‐based predictive model was constructed by logistic regression analysis. 70% of the patients were assigned to the training group, and the remaining were assigned to the validation group. The diagnostic efficacy was analyzed with receiver operating characteristic (ROC) in both the training and validation groups.

Results

For PCa versus non‐PCa, the validation model had an area under the ROC curve (AUC) of 0.985, 0.982, and 0.999 with T₂WI, ADC, and T₂WI&ADC features, respectively. For low‐grade versus high‐grade PCa, the validation model had an AUC of 0.865, 0.888, and 0.93 with T₂WI, ADC, and T₂WI&ADC features, respectively. PI‐RADS v2 had an AUC of 0.867 in differentiating PCa from non‐PCa and an AUC of 0.763 in differentiating high‐grade from low‐grade PCa.

Data Conclusion

Both the T₂WI‐ and ADC‐based radiomics models showed high diagnostic efficacy and outperformed the PI‐RADS v2 scores in distinguishing cancerous vs. noncancerous prostate tissue and high‐grade vs. low‐grade PCa.

Level of Evidence: 3

Technical Efficacy: Stage 2

J. Magn. Reson. Imaging 2019;49:875–884.