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.

Robust performance of deep learning for distinguishing glioblastoma from single brain metastasis using radiomic features: model development and validation

“…In contrast, conventional MR imaging is commonly performed for patients with neurologic symptoms in clinical practice, and thus radiomics analysis of conventional MR imaging can be used more widely. Lastly, the MR images in our study were obtained using heterogeneous MR scanner types, with various acquisition parameters, which can affect radiomic features and quantitative analysis 39 , Nonetheless, the diagnostic performance of the radiomics model remained high in the validation cohort, which substantiates the good generalizability of the model.…”

Impact of neoadjuvant androgen deprivation therapy on magnetic resonance imaging features in prostate cancer before radiotherapy

“…Grey-level patterns from radiological images, used in radiomics, have been evaluated before [18] , [19] , [28] , and GLCM and Haralick [13] , [22] , [23] , [26] features are commonly used. Daniel et al [13] studied first order- and the GLCM- features before and after ADT.…”

“…For MRI, image texture analysis has been suggested as a supporting tool to separate tumour from normal tissue [15] , even in cases with low contrast between tumour and surrounding tissue [13] . Texture features for prostate cancer [16] , [17] , [18] , [19] can be derived using grey-level co-occurrence matrices (GLCM) with the aim to separate or classify different tissue types. To do so, different statistical features can be extracted from GLCM such as Haralick features [20] , thoroughly described and applied in several studies, both in its original form [13] , [21] , and in an invariant form independent of the number of grey-levels [22] , [23] .…”

Machine Learning-Based Radiomics Predicts Radiotherapeutic Response in Patients With Acromegaly

“…Several recent studies have shown that radiomics has prospects in a broad array of applications, including early screening, accurate diagnosis, grading and staging, treatment and prognosis, and determination of molecular characteristics of brain tumors (42, 43). Radiomics has been shown to be important in predicting and assessing radiotherapeutic response in a variety of tumors, and its performance is significantly better than conventional methods (44), including lung cancer (45, 46), prostate cancer (47), rectal cancer (48). Therefore, we aimed to use a radiomics approach to predict the response of acromegaly to radiotherapy before treatment.…”