Radiomics in prostate cancer: an up-to-date review

: Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications

MR Imaging Texture Analysis in the Abdomen and Pelvis

Texture analysis (TA) is a form of radiomics and 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 MRI of the abdomen and pelvis, with the main strength being quantitative analysis of pixel intensities and heterogeneity rather than subjective/qualitative analysis. There are multiple limitations of MR texture analysis (MRTA) including a dependency on image acquisition and reconstruction parameters, non-standardized approaches without or with image filtration, diverse software methods and applications, and statistical challenges relating numerous texture analysis results to clinical outcomes in retrospective pilot studies with small sample sizes. Despite these limitations, there is a growing body of literature supporting MRTA. In this review, the application of MRTA to the abdomen and pelvis will be discussed, including tissue or tumor characterization and response evaluation or prediction of outcomes in various tumors

Hypoxic modification in the radiotherapeutic treatment for prostate cancer

Introduction: Tumour hypoxia exists among patients with prostate cancer. It is associated with resistance to radiotherapy and increased likelihood of relapse post treatment. The concurrent administration of carbogen and nicotinamide with radiotherapy has been shown to improve survival in patients with bladder cancer and selected patients with head and neck cancer, but this approach has not been attempted previously in patients with prostate cancer. Inhalation of carbogen alone can improve the oxygenation status of prostate cancer as evaluated by functional MR imaging. However, androgen deprivation therapy (ADT) causes collapse in the tumour vasculature, and patients with high risk prostate cancer routinely receive three months of androgen deprivation therapy prior to the start of their course of radical radiotherapy. The ability of carbogen administration to reverse tumour hypoxia during radiotherapy may thus be compromised. / Methods: Fifty patients with high risk prostate cancer were recruited into the single arm phase 1b/II PROCON (PROstate CarbOgen and Nicotinamide) clinical trial. They received carbogen and nicotinamide during their course of radiotherapy after they had undergone three months of neoadjuvant hormone treatment. Prevalence of urinary and gastrointestinal toxicities at two, four and twelve weeks of completing radiotherapy were recorded. PSA progression free and overall survival were calculated using the Kaplan Meier method. Twenty patients among them also underwent functional MR imaging (BOLD, diffusion weighted and dynamic contrast enhanced sequences) before and during their radiotherapy to assess the oxygenation status of their prostate cancer in response to carbogen and the state of their vasculature. / Results: None of the patients developed grade 3 or worse acute urinary or gastrointestinal toxicity, and the side effect profile is comparable to contemporary clinical trials. Despite the antivascular effect of prior hormone treatment, as confirmed by the drop in the mean Ktrans value (18-25%) following three months of ADT, the application of carbogen remained effective in reversing tumour hypoxia as demonstrated by the mean reduction in R2* value of 5.8% following the administration of carbogen. The 5 year overall survival for the entire cohort was 92%, and the 5 year PSA progression free survival was 87%. / Conclusion: The concurrent administration of carbogen and nictotinamide in patients receiving a course of radical radiotherapy for their prostate cancer is safe, and can improve tumour hypoxia despite the antivascular effect of prior hormone treatment. The 5 year PSA progression free and overall survival rates are comparable to those reported by other contemporary trials for patients with high risk prostate cancer. Future randomised clinical trials involving the use of carbogen and nicotinamide alone, or in combination with other systemic treatments, should focus on patients with hypoxic prostate cancer

3D fusion of histology to multi-parametric MRI for prostate cancer imaging evaluation and lesion-targeted treatment planning

Multi-parametric magnetic resonance imaging (mpMRI) of localized prostate cancer has the potential to support detection, staging and localization of tumors, as well as selection, delivery and monitoring of treatments. Delineating prostate cancer tumors on imaging could potentially further support the clinical workflow by enabling precise monitoring of tumor burden in active-surveillance patients, optimized targeting of image-guided biopsies, and targeted delivery of treatments to decrease morbidity and improve outcomes. Evaluating the performance of mpMRI for prostate cancer imaging and delineation ideally includes comparison to an accurately registered reference standard, such as prostatectomy histology, for the locations of tumor boundaries on mpMRI. There are key gaps in knowledge regarding how to accurately register histological reference standards to imaging, and consequently further gaps in knowledge regarding the suitability of mpMRI for tasks, such as tumor delineation, that require such reference standards for evaluation. To obtain an understanding of the magnitude of the mpMRI-histology registration problem, we quantified the position, orientation and deformation of whole-mount histology sections relative to the formalin-fixed tissue slices from which they were cut. We found that (1) modeling isotropic scaling accounted for the majority of the deformation with a further small but statistically significant improvement from modeling affine transformation, and (2) due to the depth (mean±standard deviation (SD) 1.1±0.4 mm) and orientation (mean±SD 1.5±0.9°) of the sectioning, the assumption that histology sections are cut from the front faces of tissue slices, common in previous approaches, introduced a mean error of 0.7 mm. To determine the potential consequences of seemingly small registration errors such as described above, we investigated the impact of registration accuracy on the statistical power of imaging validation studies using a co-registered spatial reference standard (e.g. histology images) by deriving novel statistical power formulae that incorporate registration error. We illustrated, through a case study modeled on a prostate cancer imaging trial at our centre, that submillimeter differences in registration error can have a substantial impact on the required sample sizes (and therefore also the study cost) for studies aiming to detect mpMRI signal differences due to 0.5 – 2.0 cm3 prostate tumors. With the aim of achieving highly accurate mpMRI-histology registrations without disrupting the clinical pathology workflow, we developed a three-stage method for accurately registering 2D whole-mount histology images to pre-prostatectomy mpMRI that allowed flexible placement of cuts during slicing for pathology and avoided the assumption that histology sections are cut from the front faces of tissue slices. The method comprised a 3D reconstruction of histology images, followed by 3D–3D ex vivo–in vivo and in vivo–in vivo image transformations. The 3D reconstruction method minimized fiducial registration error between cross-sections of non-disruptive histology- and ex-vivo-MRI-visible strand-shaped fiducials to reconstruct histology images into the coordinate system of an ex vivo MR image. We quantified the mean±standard deviation target registration error of the reconstruction to be 0.7±0.4 mm, based on the post-reconstruction misalignment of intrinsic landmark pairs. We also compared our fiducial-based reconstruction to an alternative reconstruction based on mutual-information-based registration, an established method for multi-modality registration. We found that the mean target registration error for the fiducial-based method (0.7 mm) was lower than that for the mutual-information-based method (1.2 mm), and that the mutual-information-based method was less robust to initialization error due to multiple sources of error, including the optimizer and the mutual information similarity metric. The second stage of the histology–mpMRI registration used interactively defined 3D–3D deformable thin-plate-spline transformations to align ex vivo to in vivo MR images to compensate for deformation due to endorectal MR coil positioning, surgical resection and formalin fixation. The third stage used interactively defined 3D–3D rigid or thin-plate-spline transformations to co-register in vivo mpMRI images to compensate for patient motion and image distortion. The combined mean registration error of the histology–mpMRI registration was quantified to be 2 mm using manually identified intrinsic landmark pairs. Our data set, comprising mpMRI, target volumes contoured by four observers and co-registered contoured and graded histology images, was used to quantify the positive predictive values and variability of observer scoring of lesions following the Prostate Imaging Reporting and Data System (PI-RADS) guidelines, the variability of target volume contouring, and appropriate expansion margins from target volumes to achieve coverage of histologically defined cancer. The analysis of lesion scoring showed that a PI-RADS overall cancer likelihood of 5, denoting “highly likely cancer”, had a positive predictive value of 85% for Gleason 7 cancer (and 93% for lesions with volumes \u3e0.5 cm3 measured on mpMRI) and that PI-RADS scores were positively correlated with histological grade (ρ=0.6). However, the analysis also showed interobserver differences in PI-RADS score of 0.6 to 1.2 (on a 5-point scale) and an agreement kappa value of only 0.30. The analysis of target volume contouring showed that target volume contours with suitable margins can achieve near-complete histological coverage for detected lesions, despite the presence of high interobserver spatial variability in target volumes. Prostate cancer imaging and delineation have the potential to support multiple stages in the management of localized prostate cancer. Targeted biopsy procedures with optimized targeting based on tumor delineation may help distinguish patients who need treatment from those who need active surveillance. Ongoing monitoring of tumor burden based on delineation in patients undergoing active surveillance may help identify those who need to progress to therapy early while the cancer is still curable. Preferentially targeting therapies at delineated target volumes may lower the morbidity associated with aggressive cancer treatment and improve outcomes in low-intermediate-risk patients. Measurements of the accuracy and variability of lesion scoring and target volume contouring on mpMRI will clarify its value in supporting these roles

Diffusion MRI in early cancer therapeutic response assessment

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136261/1/nbm3458_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136261/2/nbm3458.pd

Multi-parametric MRI to guide salvage treatment of recurrent prostate cancer

Prostate cancer (PCa) is frequently treated with radiotherapy. However, depending on the aggressiveness of the disease, the risk of recurrence can be up to 35% within five years of the initial treatment. Patients with localised recurrent PCa are candidates for curative (i.e. salvage) treatment. To overcome the toxicity associated with whole-gland approaches, focal salvage treatments target the index lesion while sparing the surrounding tissue. The studies described in this thesis elaborate on the use of quantitative multi-parametric MRI (mp-MRI) for the detection and localisation of locally recurrent PCa after radiotherapy. Pre-treatment radiomic imaging features were found to have potential to improve recurrence-risk prediction models for high-risk PCa patients treated with radiotherapy. In this thesis, the mp-MRI properties of irradiated benign tissue and recurrent tumour were characterised, with access to pathological samples. These findings can be used as a foundation to establish guidelines (which are currently absent) on how to assess and score MRI scans after radiotherapy. Improving radiological knowledge in the recurrent setting can lead to improved staging and result in better patient selection for salvage treatments. Lastly, this thesis provides evidence on how best to define the region to target, leading to a refinement of focal salvage strategies.KWF KankerbestrijdingLUMC / Geneeskund

Animal models and their role in imaging-assisted co-clinical trials

The availability of high-fidelity animal models for oncology research has grown enormously in recent years, enabling preclinical studies relevant to prevention, diagnosis, and treatment of cancer to be undertaken. This has led to increased opportunities to conduct co-clinical trials, which are studies on patients that are carried out parallel to or sequentially with animal models of cancer that mirror the biology of the patients\u27 tumors. Patient-derived xenografts (PDX) and genetically engineered mouse models (GEMM) are considered to be the models that best represent human disease and have high translational value. Notably, one element of co-clinical trials that still needs significant optimization is quantitative imaging. The National Cancer Institute has organized a Co-Clinical Imaging Resource Program (CIRP) network to establish best practices for co-clinical imaging and to optimize translational quantitative imaging methodologies. This overview describes the ten co-clinical trials of investigators from eleven institutions who are currently supported by the CIRP initiative and are members of the Animal Models and Co-clinical Trials (AMCT) Working Group. Each team describes their corresponding clinical trial, type of cancer targeted, rationale for choice of animal models, therapy, and imaging modalities. The strengths and weaknesses of the co-clinical trial design and the challenges encountered are considered. The rich research resources generated by the members of the AMCT Working Group will benefit the broad research community and improve the quality and translational impact of imaging in co-clinical trials

Development and validation of novel and quantitative MRI methods for cancer evaluation

Quantitative imaging biomarkers (QIB) offer the opportunity to further the evaluation of cancer at presentation as well as predict response to anti-cancer therapies before and early during treatment with the ultimate goal of truly personalised medical care and the mitigation of futile, often detrimental, therapy. Few QIBs are successfully translated into clinical practice and there is increasing recognition that rigorous methodologies and standardisation of research pipelines and techniques are required to move a theoretically useful biomarker into the clinic. To this end, I have aimed to give an overview of what I believe to be some of key elements within the research field beginning with the concept of imaging biomarkers, introducing concepts in development and validation, before providing a summary of the current and future utility of a range of quantitative MR imaging biomarkers techniques within the oncological imaging field. The original, prospective, research moves from the technical and analytical validation of a novel QIB use (T1 mapping in cancer), first in vivo qualification of this biomarker in cancer patient response assessment and prediction (sarcoma and breast cancer as well as prostate cancer separately), and then moving on to application of more established QIBs in cancer evaluation (R2*/BOLD imaging in head and neck cancer) as well as how existing MR data can be post-processed to improved cancer evaluation (further metrics derived from diffusion weighted imaging in head and neck cancer and textural analysis of existing clinical MR images utility in prostate cancer detection)