Magnetic resonance imaging (MRI)-based radiomics for prostate cancer radiotherapy

Abstract

In radiotherapy (RT) of prostate cancer, dose escalation has been shown to reduce biochemical failure. Dose escalation only to determinate prostate tumor habitats has the potential to improve tumor control with less toxicity than when the entire prostate is dose escalated. Other issues in the treatment of the RT patient include the choice of the RT technique (hypo- or standard fractionation) and the use and length of concurrent/adjuvant androgen deprivation therapy (ADT). Up to 50% of high-risk men demonstrate biochemical failure suggesting that additional strategies for defining and treating patients based on improved risk stratification are required. The use of multiparametric MRI (mpMRI) is rapidly gaining momentum in the management of prostate cancer because of its improved diagnostic potential and its ability to combine functional and anatomical information. Currently, the Prostate Imaging, Reporting and Diagnosis System (PIRADS) is the standard of care for region of interest (ROI) identification and risk classification. However, PIRADS was not designed for 3D tumor volume delineation; there is a large degree of subjectivity and PIRADS does not accurately and reproducibly elucidate inter- and intra-lesional spatial heterogeneity. "Radiomics", as it refers to the extraction and analysis of large number of advanced quantitative radiological features from medical images using high throughput methods, is perfectly suited as an engine to effectively sift through the multiple series of prostate mpMRI sequences and quantify regions of interest. The radiomic efforts can be summarized in two main areas: (I) detection/segmentation of the suspicious lesion; and (II) assessment of the aggressiveness of prostate cancer. As related to RT, the goal of the latter is in particular to identify patients at high risk for metastatic disease; and the aim of the former is to identify and segment cancerous lesions and thus provide targets for radiation boost. The article is structured as follows: first, we describe the radiomic approach; and second, we discuss the radiomic pipeline as tailored for RT of prostate cancer. In this process we summarize the current efforts and progress in integrating mpMRI radiomics into the radiotherapeutic management of prostate cancer with emphasis placed on its role in treatment target definition, treatment plan strategizing, and prognostic assessment. The described concepts, methods and tools are not currently applicable to the radiation oncology practice outside of the research setting. More data are required in the form of clinical trials to assess the robustness of radiomics-based predictive models, and to maximize the efficacy of these models.

Keywords: Prostate cancer; multiparametric magnetic resonance imaging (mpMRI); radiomics; radiotherapy (RT).

Figure 1

mpMRI radiomics in prostate RT. (A) mpMRI exam of the prostate typically includes acquisition of T2-weighted (T2w), diffusion-weighted imaging (DWI) and the associated apparent diffusion coefficient (ADC) map and dynamic contrast enhanced (DCE)-MRI. In this panel the T2w, DWI at high b-value (1,000 s/mm²), ADC and the early enhancing image in the DCE-MRI from radiotherapy patient is shown. The red arrows indicate a tumor in the peripheral zone (PZ). The tumor appears darker on T2w, brighter on the high b-value DWI and it is characterized with reduced diffusion (ADC) and increased perfusion (DCE-MRI); (B) segmentation of volumes of interest (VOI) in prostate cancer radiotherapy generally involves identification of prostate, urethra, PZ, transition zone (TZ), as shown in the top panel, and differentiation of normal appearing tissues (NAT) in PZ and TZ, as shown in the middle panel, along with delineation of the gross tumor volume (GTV) in 3D, as shown in the bottom panel; (C) radiomics features extracted from prostate mpMRI can be grouped into four major categories related, respectively, to semantic, morphological, statistical, and transform analysis. Semantic features refer to quantitative descriptors derived by the radiologists empirically when assessing mpMRI, morphological features are measures of geometrical shape and physical composition of the segmented VOIs, statistical features quantify the gray level intensity distribution and/or spatial relations between image voxels inside VOIs, and transform-based features depict repetitive or non-repetitive spatial patterns through mathematical transformation to the segmented image content; (D) to achieve holistic models, radiomics features should be integrated with other available biomarkers, such as data from clinical records, genomic profiling, proteomic screening, and physiological analysis; (E) application of integrated data/models could span the entire range of radiotherapy for prostate cancer, from aiding in diagnostic establishment to facilitating patient-individualized treatment strategizing to improving predictive and prognostic accuracy.

Figure 2

Implementation of Habitat Risk Score (HRS) in radiotherapy planning of a BLaStM patient. (A) An axial slice of the prostate on T2-weighted (T2w) MRI. The red arrow indicates an anterior tumor, characterized with hypo-intensity in the T2w; (B) corresponding slice of apparent diffusion coefficient (ADC) map with area of restricted diffusion (red arrow); (C) early enhancing image from the dynamic contrast enhanced (DCE)-MRI showing increased perfusion in the tumor area (red arrow); (D) Habitat Risk Score (HRS), represented as a heat-map, overlaid on the T2w. HRS is calculated in MIM, using Java plug-in. The approach scores every pixel with a 10-point scale (insert) in increasing risk for cancer. The volume of HRS10 is empty; (E) the 3D volume of the tumor, as depicted by HRS6 is used for gross tumor volume (GTV), the red arrow pointing to a fiducial marker; (F) the planning CT is aligned to the T2w, using fiducial matching (red arrow). The final result is displayed where the smoothed HRS6 contour has been migrated to the planning CT.