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We investigated the longitudinal changes in multiparametric MRI (mpMRI) (T2-weighted, Apparent Diffusion Coefficient (ADC), and Dynamic Contrast Enhanced (DCE-)MRI) of prostate cancer patients receiving Lattice Extreme Ablative Dose (LEAD) radiotherapy (RT) and the capability of their imaging features to predict RT outcome based on endpoint biopsies. Ninety-five mpMRI exams from 25 patients, acquired pre-RT and at 3-, 9-, and 24-months post-RT were analyzed. MRI/Ultrasound-fused biopsies were acquired pre- and at two-years post-RT (endpoint). Five regions of interest (ROIs) were analyzed: Gross tumor volume (GTV), normally-appearing tissue (NAT) and peritumoral volume in both peripheral (PZ) and transition (TZ) zones. Diffusion and perfusion radiomics features were extracted from mpMRI and compared before and after RT using two-tailed Student t-tests. Selected features at the four scan points and their differences (Δ radiomics) were used in multivariate logistic regression models to predict the endpoint biopsy positivity. Baseline ADC values were significantly different between GTV, NAT-PZ, and NAT-TZ (p-values < 0.005). Pharmaco-kinetic features changed significantly in the GTV at 3-month post-RT compared to baseline. Several radiomics features at baseline and three-months post-RT were significantly associated with endpoint biopsy positivity and were used to build models with high predictive power of this endpoint (AUC = 0.98 and 0.89, respectively). Our study characterized the RT-induced changes in perfusion and diffusion. Quantitative imaging features from mpMRI show promise as being predictive of endpoint biopsy positivity.

PurposeRecent integration of open-source data to machine learning models, especially in the medical field, has opened new doors to study disease progression and/or regression. However, the limitation of using medical data for machine learning approaches is the specificity of data to a particular medical condition. In this context, most recent technologies like generative adversarial networks (GAN) could be used to generate high quality synthetic data that preserves the clinical variability.Materials and MethodsIn this study, we used 139 T2-weighted prostate magnetic resonant images (MRI) from various sources as training data for Single Natural Image GAN (SinGAN), to make a generative model. A deep learning semantic segmentation pipeline trained the model to segment the prostate boundary on 2D MRI slices. Synthetic images with a high-level segmentation boundary of the prostate were filtered and used in the quality control assessment by participating scientists with varying degree of experience (more than 10 years, 1 year, or no experience) to work with MRI images.ResultsThe most experienced participating group correctly identified conventional vs synthetic images with 67% accuracy, the group with 1 year of experience correctly identified the images with 58% accuracy, and group with no prior experience reached 50% accuracy. Nearly half (47%) of the synthetic images were mistakenly evaluated as conventional images. Interestingly, a blinded quality assessment by a board-certified radiologist to differentiate conventional and synthetic images was not significantly different in context of the mean quality of synthetic and conventional images.ConclusionsThis study shows promise that high quality synthetic images from MRI can be generated using GAN. Such an AI model may contribute significantly to various clinical applications which involves supervised machine learning approaches.

Purpose: To investigate the relationships between circulating tumor cells (CTCs), free circulating DNA (fcDNA) and biochemical response in prostate cancer patients treated primarily versus salvage radiotherapy (RT). Methods and Materials: Blood was collected prospectively from patients, enrolled in two institutional Phase II trials for primary and salvage RT. Three blood samples were collected at: (i) prior to treatment [RT or androgen deprivation therapy (ADT)], (ii) last week of RT, and (iii) three months post-RT. CTCs were quantified in 31 samples from 12 primary patients and 30 samples from 12 salvage patients; fcDNA were analyzed in 11 primary (28 samples) and 5 (9 samples) salvage patients. CTCs were visualized by immunofluorescence after microfilter capture and fcDNA was quantified using real-time Polymerase chain reaction (PCR). CTCs and fcDNA were correlated with early biochemical response by subdividing patients into early favorable and unfavorable response at 3 months after RT. Results: For those treated primarily, there was a direct correlation with CTC counts and prostate specific antigen (PSA) pre-RT that changed to a reciprocal relationship 3 months post-RT. CTCs increased significantly (p=0.03) at 3 months after primary RT in the biochemical favorable patients, while no significant association was observed for fcDNA. Correspondingly, post-RT fcDNA levels were inversely related to CTC counts. In salvage patients, the number of CTCs was related to pre-RT PSA, but it was not correlated to RT response. In post-RT series, a significant direct correlation was observed between CTCs and PSA. Conclusion: Our preliminary studies suggest that RT affects CTC counts, which are thus associated with prostate cancer biochemical response. A larger cohort with longer follow-up will be needed to establish the association with more recognized treatment endpoints.

Purpose/Objective(s) Recently identified changes in brain during daily MRI-guided radiation therapy (MRgRT) of patients with glioblastoma (GBM) can permit adaptive radiotherapy. However, daily changes can be subtle to detect and difficult to evaluate volumetrically without significant manual effort. The aim of this study is to develop a deep learning (DL) algorithm for automatic volume delineation of daily MRI scans from a hybrid MRI-RT system as an early warning system of tumor change and to facilitate analysis of delta radiomics and clinical trials based on MRI changes during RT. Materials/Methods GBM patients undergoing 30 fractions (6 weeks) of RT delivered by a combination MRI-RT system with concurrent temozolomide (TMZ) treatment were considered for study. The MRI data for each patient consisted of one predominantly T2-weighted balanced steady state free precession (bSSFP) planning scan (isotropic 1.5 mm3 resolution, 128 sec) followed by 30 daily bSSFP setup scans for 30 fractions of RT for each patient. Tumor lesion (TL) with/without surrounding edema, and resection cavity (RC) were manually contoured for each patient on each of the 31 scans in MIM. The DL-generated masks were converted to 3D volumes and compared using Dice Similarity Coefficient (DSC) between manual and automatic contours. In addition, the correlation between the automatic and manual volumes was examined with Person Correlation Coefficient (r). Results The MRIs of 14 patients undergoing MRgRT with biopsy, subtotal resection, or gross total resection of GBM were incorporated into a dataset. Seven patients had only TL, 3 patients had both TL and RC, and 4 patients had RC only. MRIs from 11 patients were used in training and 3 patients were left for testing. Total of 8,505 axial slices were used to train the network, with 1,021 slices used as validation. DSC for TL and RC on the training and test datasets were: mean ± standard deviation: 0.87 ± 0.128 and 0.9 ± 0.122; 0.74 ± 0.233 and 0.8 ± 0.277, respectively. The CNN achieved DSC greater than 0.8 in 2 of the testing patients but less than 0.8 on 1, indicating that while successful, the network was not yet well generalized with the current training data. The automated lesion volumes correlated significantly with the manual volumes with r value of 0.9. Conclusion Contouring of brain lesions is a complex and time-consuming task, which impacts research throughput as it needs to be performed for every patient and scan. We have shown the promise of MASK R-CNN to produce automated contours for brain tumors. As more patients are treated with MRI-RT and with additional contrasts, the network will improve the accuracy and its generalizability. The use of DL in RT research improves the feasibility of visualizing tumor kinetics trends in real time to alert the physician to significant changes and further study of delta radiomics.

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.

PURPOSE: Develop a deep-learning-based segmentation algorithm for prostate and its peripheral zone (PZ) that is reliable across multiple MRI vendors. METHODS: This is a retrospective study. The dataset consisted of 550 MRIs (Siemens-330, General Electric[GE]-220). A multistream 3D convolutional neural network is used for automatic segmentation of the prostate and its PZ using T2-weighted (T2-w) MRI. Prostate and PZ were manually contoured on axial T2-w. The network uses axial, coronal, and sagittal T2-w series as input. The preprocessing of the input data includes bias correction, resampling, and image normalization. A dataset from two MRI vendors (Siemens and GE) is used to test the proposed network. Six different models were trained, three for the prostate and three for the PZ. Of the three, two were trained on data from each vendor separately, and a third (Combined) on the aggregate of the datasets. The Dice coefficient (DSC) is used to compare the manual and predicted segmentation. RESULTS: For prostate segmentation, the Combined model obtained DSCs of 0.893 ± 0.036 and 0.825 ± 0.112 (mean ± standard deviation) on Siemens and GE, respectively. For PZ, the best DSCs were from the Combined model: 0.811 ± 0.079 and 0.788 ± 0.093. While the Siemens model underperformed on the GE dataset and vice versa, the Combined model achieved robust performance on both datasets. CONCLUSION: The proposed network has a performance comparable to the interexpert variability for segmenting the prostate and its PZ. Combining images from different MRI vendors on the training of the network is of paramount importance for building a universal model for prostate and PZ segmentation.

A procedure for identification of optimal Apparent Diffusion Coefficient (ADC) thresholds for automatic delineation of prostatic lesions with restricted diffusion at differing risk for cancer was developed. The relationship between the size of the identified Volumes of Interest (VOIs) and Gleason Score (GS) was evaluated. Patients with multiparametric (mp)MRI, acquired prior to radical prostatectomy (RP) (n = 18), mpMRI-ultrasound fused (MRI-US) (n = 21) or template biopsies (n = 139) were analyzed. A search algorithm, spanning ADC thresholds in 50 µm2/s increments, determined VOIs that were matched to RP tumor nodules. Three ADC thresholds for both peripheral zone (PZ) and transition zone (TZ) were identified for estimation of VOIs at low, intermediate, and high risk of prostate cancer. The determined ADC thresholds for low, intermediate and high risk in PZ/TZ were: 900/800; 1100/850; and 1300/1050 µm2/s. The correlation coefficients between the size of the high/intermediate/low risk VOIs and GS in the three cohorts were 0.771/0.778/0.369, 0.561/0.457/0.355 and 0.423/0.441/0.36 (p

To develop a prostate tumor habitat risk scoring (HRS) system based on multiparametric magnetic resonance imaging (mpMRI) referenced to prostatectomy Gleason score (GS) for automatic delineation of gross tumor volumes. A workflow for integration of HRS into radiation therapy boost volume dose escalation was developed in the framework of a phase 2 randomized clinical trial (BLaStM).An automated quantitative mpMRI-based 10-point pixel-by-pixel method was optimized to prostatectomy GSs and volumes using referenced dynamic contrast-enhanced and apparent diffusion coefficient sequences. The HRS contours were migrated to the planning computed tomography scan for boost volume generation.There were 51 regions of interest in 12 patients who underwent radical prostatectomy (26 with GS ≥7 and 25 with GS 6). The resultant heat maps showed inter- and intratumoral heterogeneity. The HRS6 level was significantly associated with radical prostatectomy regions of interest (slope 1.09, r = 0.767; P.0001). For predicting the likelihood of cancer, GS ≥7 and GS ≥8 HRS6 area under the curve was 0.718, 0.802, and 0.897, respectively. HRS was superior to the Prostate Imaging, Reporting and Diagnosis System 4/5 classification, wherein the area under the curve was 0.62, 0.64, and 0.617, respectively (difference with HR6, P.0001). HRS maps were created for the first 37 assessable patients on the BLaStM trial. There were an average of 1.38 habitat boost volumes per patient at a total boost volume average of 3.6 cmAn automated quantitative mpMRI-based method was developed to objectively guide dose escalation to high-risk habitat volumes based on prostatectomy GS.

Magnetic resonance-guided radiotherapy (MRgRT) is a new and evolving treatment modality that allows unprecedented visualization of the tumor and surrounding anatomy. MRgRT includes daily 3D magnetic resonance imaging (MRI) for setup and rapidly repeated near real-time MRI scans during treatment for target tracking. One of the more exciting potential benefits of MRgRT is the ability to analyze serial MRIs to monitor treatment response or predict outcomes. A typical radiation treatment (RT) over the span of 10-15 minutes on the MRIdian system (ViewRay, Cleveland, OH) yields thousands of “cine” images, each acquired in 250 ms. This unique data allows for a glimpse in image intensity changes during RT delivery. In this report, we analyze cine images from a single fraction RT of a glioblastoma patient on the ViewRay platform in order to characterize the dynamic signal changes occurring during RT therapy. The individual frames in the cines were saved into DICOM format and read into an MIM image analysis platform (MIM Software, Cleveland, OH) as a time series. The three possible states of the three Cobalt-60 radiation sources—OFF, READY, and ON—were also recorded. An in-house Java plugin for MIM was created in order to perform principal component analysis (PCA) on each of the datasets. The analysis resulted in first PC, related to monotonous signal increase over the course of the treatment fraction. We found several distortion patterns in the data that we postulate result from the perturbation of the magnetic field due to the moving metal parts in the platform while treatment was being administered. The largest variations were detected when all Cobalt-60 sources were OFF. During this phase of the treatment, the gantry and multi-leaf collimators (MLCs) are moving. Conversely, when all Cobalt-60 sources were in the ON position, the image signal fluctuations were minimal, relating to very little mechanical motion. At this phase, the gantry, the MLCs, and sources are fixed in their positions. These findings were confirmed in a study with the daily quality assurance (QA) phantom. While the identified variations were not related to physiological processes, our findings confirm the sensitivity of the developed approach to identify very small fluctuations. Relating these variations to the physical changes that occur during treatment shows the methodical ability of the technique to uncover their underlying sources.

We present a radiomics-based approach developed for the SPIE-AAPM-NCI PROSTATEx challenge. The task was to classify clinically significant prostate cancer in multiparametric (mp) MRI. Data consisted of a "training dataset" (330 suspected lesions from 204 patients) and a "test dataset" (208 lesions/140 patients). All studies included T2-weighted (T2-W), proton density-weighted, dynamic contrast enhanced, and diffusion-weighted imaging. Analysis of the images was performed using the MIM imaging platform (MIM Software, Cleveland, Ohio). Prostate and peripheral zone contours were manually outlined on the T2-W images. A workflow for rigid fusion of the aforementioned images to T2-W was created in MIM. The suspicious lesion was outlined using the high b-value image. Intensity and texture features were extracted on four imaging modalities and characterized using nine histogram descriptors: 10%, 25%, 50%, 75%, 90%, mean, standard deviation, kurtosis, and skewness (216 features). Three classification methods were used: classification and regression trees (CART), random forests, and adaptive least absolute shrinkage and selection operator (LASSO). In the held out by the organizers test dataset, the areas under the curve (AUCs) were: 0.82 (random forests), 0.76 (CART), and 0.76 (adaptive LASSO). AUC of 0.82 was the fourth-highest score of 71 entries (32 teams) and the highest for feature-based methods.

Purpose: To determine whether blood oxygenation level dependent (BOLD) MRI signal measured in prostate cancer patients, in addition to quantitative diffusion and perfusion parameters from multiparametric (mp)MRI exams, can help discriminate aggressive and/or radioresistant lesions. Methods: Several ongoing clinical trials in our institution require mpMRI exam to determine eligibility (presence of identifiable tumor lesion on mpMRI) and prostate volumes for dose escalation. Upon consent, patients undergo fiducial markers placement and a T2*-weighted imaging at the time of CT sim to facilitate the fusion. In a retrospective analysis eleven clinical trial patients were identified who had undergone mpMRI on GE 3T magnet, followed by T2*-weighted imaging (time-period mean±SD = 48±20 days) using a consistent protocol (gradient echo, TR/TE=30/11.8ms, flip angle=12, matrix=256×256×75, voxel size=1.25×1.25×2.5mm). ROIs for prostate tumor lesions were automatically determined using ADC threshold ≤1200 µm2/s. Although the MR protocol was not intended for BOLD analysis, we utilized the T2*-weighted signal normalized to that in nearby muscle; likewise, T2-weighted lesion signal was normalized to muscle, following rigid registration of the T2 to T2* images. The ratio of these normalized signals, T2*/T2, is a measure of BOLD effect in the prostate tumors. Perfusion parameters (Ktrans, ve, kep) were also calculated. Results: T2*/T2 (mean±SE) was found to be substantially lower for Gleason score (GS) 8&9 (0.82±0.04) compared to GS 7 (1.08±0.07). A k-means cluster analysis of T2*/T2 versus kep = Ktrans/ve revealed two distinct clusters, one with higher T2*/T2 and lower kep, containing only GS 7 lesions, and another with lower T2*/T2 and higher kep, associated with tumor aggressiveness. This latter cluster contained all GS 8&9 lesions, as well as some GS 7. Conclusion: BOLD MRI, in addition to ADC and kep, may play a role (perhaps orthogonal to Gleason score) in identifying prostate lesions that would benefit from more aggressive radiotherapy.