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Highlights • This paper evaluated lesion-directed prostatic high dose rate brachytherapy. • Lesions defined by prostate specific membrane antigen positron emission tomography. • Dose escalation was confirmed using whole-mount digital histology. • Targeting lesions led to significantly higher dose to high-grade histologic cancer., Background and purpose Prostate specific membrane antigen positron emission tomography imaging (PSMA-PET) has demonstrated potential for intra-prostatic lesion localization. We leveraged our existing database of co-registered PSMA-PET imaging with cross sectional digitized pathology to model dose coverage of histologically-defined prostate cancer when tailoring brachytherapy dose escalation based on PSMA-PET imaging. Materials and methods Using a previously-developed automated approach, we created segmentation volumes delineating underlying dominant intraprostatic lesions for ten men with co-registered pathology-imaging datasets. To simulate realistic high-dose-rate brachytherapy (HDR-BT) treatments, we registered the PSMA-PET-defined segmentation volumes and underlying cancer to 3D trans-rectal ultrasound images of HDR-BT cases where 15 Gray (Gy) was delivered. We applied dose/volume optimization to focally target the dominant intraprostatic lesion identified on PSMA-PET. We then compared histopathology dose for all high-grade cancer within whole-gland treatment plans versus PSMA-PET-targeted plans. Histopathology dose was analyzed for all clinically significant cancer with a Gleason score of 7or greater. Results The standard whole-gland plans achieved a median [interquartile range] D98 of 15.2 [13.8–16.4] Gy to the histologically-defined cancer, while the targeted plans achieved a significantly higher D98 of 16.5 [15.0–19.0] Gy (p = 0.007). Conclusion This study is the first to use digital histology to confirm the effectiveness of PSMA-PET HDR-BT dose escalation using automatically generated contours. Based on the findings of this study, PSMA-PET lesion dose escalation can lead to increased dose to the ground truth histologically defined cancer.

Prostate cancer (PCa) is a common, serious form of cancer in men that is still prevalent despite ongoing developments in diagnostic oncology. Current detection methods lead to high rates of inaccurate diagnosis. We present a method to directly model and exploit temporal aspects of temporal enhanced ultrasound (TeUS) for tissue characterization, which improves malignancy prediction. We employ a probabilistic-temporal framework, namely, hidden Markov models (HMMs), for modeling TeUS data obtained from PCa patients. We distinguish malignant from benign tissue by comparing the respective log-likelihood estimates generated by the HMMs. We analyze 1100 TeUS signals acquired from 12 patients. Our results show improved malignancy identification compared to previous results, demonstrating over 85% accuracy and AUC of 0.95. Incorporating temporal information directly into the models leads to improved tissue differentiation in PCa. We expect our method to generalize and be applied to other types of cancer in which temporal-ultrasound can be recorded.

Purpose Localized prostate cancer (PCa) in patients is characterized by a dominant focus in the gland (dominant intraprostatic lesion, DIL). Accurate DIL identification may enable more accurate diagnosis and therapy through more precise targeting of biopsy, radiotherapy and focal ablative therapies. The goal of this study is to validate the performance of [18F]DCFPyL PET and CT perfusion (CTP) for detecting and localizing DIL against digital histopathological images. Methods Multi-modality image sets: in vivo T2-weighted (T2w)-MRI, 22-min dynamic [18F]DCFPyL PET/CT, CTP, and 2-h post-injection PET/MR were acquired in patients prior to radical prostatectomy. The explanted gland with implanted fiducial markers was imaged with T2w-MRI. All images were co-registered to the pathologist-annotated digital images of whole-mount mid-gland histology sections using fiducial markers and anatomical landmarks. Regions of interest encompassing DIL and non-DIL tissue were drawn on the digital histopathological images and superimposed on PET and CTP parametric maps. Logistic regression with backward elimination of parameters was used to select the most sensitive parameter set to distinguish DIL from non-DIL voxels. Leave-one-patient-out cross-validation was performed to determine diagnostic performance. Results [18F]DCFPyL PET and CTP parametric maps of 15 patients were analyzed. SUVLate and a model combining Ki and k4 of [18F]DCFPyL achieved the most accurate performance distinguishing DIL from non-DIL voxels. Both detection models achieved an AUC of 0.90 and an error rate of i and k4 model and 0.7 for SUVLate. Conclusions We have validated using co-registered digital histopathological images that parameters from kinetic analysis of 22-min dynamic [18F]DCFPyL PET can accurately localize DILs in PCa for targeting of biopsy, radiotherapy, and focal ablative therapies. Short-duration dynamic [18F]DCFPyL PET was not inferior to SUVLate in this diagnostic task. Clinical trial registration number: NCT04009174 (ClinicalTrials.gov).

BACKGROUND Overtreatment of prostate cancer (PCa) is a healthcare issue. Development of noninvasive imaging tools for improved characterization of prostate lesions might reduce overtreatment. PURPOSE To measure the distribution of tissue sodium concentration (TSC), proton T2 -weighted signal, and apparent diffusion coefficient (ADC) values in human PCa and to test the presence of a correlation between regional differences in imaging metrics and the Gleason grade of lesions determined from histopathology. STUDY TYPE Cross-sectional. SUBJECTS Ten men with biopsy-proven PCa. SEQUENCES/FIELD STRENGTH Sodium, proton T2 -weighted, and diffusion-weighted MRI data were acquired using Broad-Band 3D-Fast-Gradient-Recalled, 3D Cube (Isotropic 3D-Fast-Turbo-Spin-Echo acquisition) and 2D Spin-Echo sequences, respectively, with a 3.0T MR scanner. ASSESSMENT All imaging data were coregistered to Gleason-graded postprostatectomy histology, as the standard for prostate cancer lesion characterization. Regional TSC and T2 data were assessed using percent changes from healthy tissue of the same patient (denoted ΔTSC, ΔT2 ). STATISTICS Differences in ΔTSC, ADC, and ΔT2 as a function of Gleason score were analyzed for each imaging contrast using a one-way analysis of variance or a nonparametric t-test. Correlations between imaging data measures and Gleason score were assessed using a Spearman's ranked correlation. RESULTS Evaluation of the correlation of ΔTSC, ADC, and ΔT2 datasets with Gleason scoring revealed that only the correlation between ΔTSC and Gleason score was statistically significant (rs = 0.791, p

An ongoing prospective study is acquiring preoperative imaging data for men with prostate cancer (PCa) using the molecular imaging agent [ F]-DCFPyL targeted against prostate-specific membrane antigen (PSMA). To date, six men (of a planned accrual of 24) with clinically localized, biopsy-proven PCa have undergone preoperative [ F]-DCFPyL positron emission tomography (PET) imaging and multiparametric magnetic resonance imaging acquired using a hybrid PET/MRI system. Lesions identified by [ F]-DCFPyL uptake on PET/MRI were characterized in terms of maximum standardized uptake value (SUV ) and volume using a boundary threshold of 40% SUV . Following surgery, all prostatectomy specimens were processed using a whole-mount technique for accurate deformable co-registration and correlation with PCa foci defined on digitized pathology images. Well-defined intraprostatic dominant lesions were identified by [ F]-DCFPyL PET/MRI (mean SUV 11.4 ± 8.25; mean volume 2.2 ± 2.4 cm ) in all six men. Co-registered digitized whole-mount pathology for the first case revealed that intense [ F]-DCFPyL uptake (SUV 27 ± 1.1 cm ) and multiparametric MRI changes (Prostate Imaging Reporting and Data System score of 4) were highly correlated with a 0.5-cm dominant (largest) lesion with Gleason pattern 4 PCa in the right mid peripheral zone. A smaller focus (0.01 cm ) of lower-grade PCa (Gleason pattern 3) had much lower uptake (SUV 2.7). These early prospective data show that dominant intraprostatic lesions could be identified in all six men using [ F]-DCFPyL as an imaging probe. Trial accrual will continue to quantify in terms of spatial concordance the ability of [ F]-DCFPyL to identify the location and characterize the grade of intraprostatic cancer foci in clinically localized PCa. Patient summary: Positron emission tomography using a novel probe called [ F]-DCFPyL directed against the prostate-specific membrane antigen protein was able to identify locations of prostate cancer in the prostate glands of men undergoing imaging before surgery. In the future, such imaging may allow better targeting of treatment to the portion of the prostate containing the most aggressive components of cancer rather than treating the whole prostate in a uniform fashion. [ F]-DCFPyL positron emission tomography/magnetic resonance imaging shows promise for identifying high-grade intraprostatic foci of prostate cancer in a spatially accurate fashion to facilitate targeted biopsy and treatment. 18 18 18 18 3 18 3 3 3 18 18 18 18 max max max max

In-vivo imaging of the prostate has shown to be useful for prostate cancer (PCa) localization especially during biopsy procedures. Multi-parametric MRI (mp-MRI) is gaining rapid popularity amongst clinicians but is complex and difficult to interpret by even expert radiologists. Prostate specific membrane antigen positron emission tomography (PSMA PET) is emerging as a new tool for PCa detection and has shown promising results towards lesion identification. Both imaging procedures suffer from intra- and inter- observer variability in PCa detection. Computer-aided diagnosis (CAD) systems have been developed as a solution to mitigate observer variability and have shown to boost diagnostic accuracy. There are currently no studies published that assessed the benefit of incorporating PSMA PET imaging and mp-MRI into a CAD system for PCa detection. We compared the accuracy of CAD models trained and tested on features from mp-MRI+PSMA PET, mp-MRI and PSMA PET by training on 1-10 features chosen from three feature selection methods for 10 different classifiers for each of the three experiments. We found that models trained on mp-MRI provided lower overall error and greater specificity, and models trained on mp-MRI+PSMA PET and PSMA PET provided greater sensitivity to lesions in the central gland, which is a known area of difficulty for mp-MRI. Further validation using a larger dataset is required to prove the added benefit of PSMA PET imaging as a second modality to PCa CAD systems. Once fully validated, these results will demonstrate the added benefit of incorporating PSMA PET imaging into CAD models towards PCa detection.

Automatically detecting and grading cancerous regions on radical prostatectomy (RP) sections facilitates graphical and quantitative pathology reporting, potentially benefitting post-surgery prognosis, recurrence prediction, and treatment planning after RP. Promising results for detecting and grading prostate cancer on digital histopathology images have been reported using machine learning techniques. However, the importance and applicability of those methods have not been fully investigated. We computed three-class tissue component maps (TCMs) from the images, where each pixel was labeled as nuclei, lumina, or other. We applied seven different machine learning approaches: three non-deep learning classifiers with features extracted from TCMs, and four deep learning, using transfer learning with the 1) TCMs, 2) nuclei maps, 3) lumina maps, and 4) raw images for cancer detection and grading on whole-mount RP tissue sections. We performed leave-one-patient-out cross-validation against expert annotations using 286 whole-slide images from 68 patients. For both cancer detection and grading, transfer learning using TCMs performed best. Transfer learning using nuclei maps yielded slightly inferior overall performance, but the best performance for classifying higher-grade cancer. This suggests that 3-class TCMs provide the major cues for cancer detection and grading primarily using nucleus features, which are the most important information for identifying higher-grade cancer.

Prostate cancer (PCa) is a common, serious form of cancer in men that is still prevalent despite ongoing developments in diagnostic oncology. Current detection methods lead to high rates of inaccurate diagnosis. We present a method to directly model and exploit temporal aspects of temporal enhanced ultrasound (TeUS) for tissue characterization, which improves malignancy prediction. We employ a probabilistic-temporal framework, namely, hidden Markov models (HMMs), for modeling TeUS data obtained from PCa patients. We distinguish malignant from benign tissue by comparing the respective log-likelihood estimates generated by the HMMs. We analyze 1100 TeUS signals acquired from 12 patients. Our results show improved malignancy identification compared to previous results, demonstrating over 85% accuracy and AUC of 0.95. Incorporating temporal information directly into the models leads to improved tissue differentiation in PCa. We expect our method to generalize and be applied to other types of cancer in which temporal-ultrasound can be recorded.

Multi-parametric MRI (mp-MRI) has shown to be useful in contemporary prostate biopsy procedures. Unfortunately, mp-MRI is relatively complex to interpret and suffers from inter-observer variability in lesion localization and grading. Computer-aided diagnosis (CAD) systems have been developed as a potential solution and have been shown to boost diagnostic accuracy. We measured the accuracy of a CAD model using a systematic sampling algorithm to remove any spatial bias present in our input. We trained four classifiers with 1-10 features chosen by forward feature selection for each and reported the system with the highest AUC in both the peripheral zone and central gland. Furthermore, we investigated the effect on system performance by varying the minimum tumour size threshold and by varying the average difference in area between malignant and healthy tissue samples. The CAD model was able to classify malignant vs. benign tissue with accuracies competitive with those reported in the literature. Eroding healthy tissue ROIs positively biased the system's performance for the PZ, but no such trend was found in the CG. Once fully validated, this system has the potential to imp.

Objectives : Temporal enhanced ultrasound (TeUS) is a new ultrasound-based imaging technique that provides tissue-specific information. Recent studies have shown the potential of TeUS for improving tissue characterization in prostate cancer diagnosis. We study the temporal properties of TeUS—temporal order and length—and present a new framework to assess their impact on tissue information. Methods : We utilize a probabilistic modeling approach using hidden Markov models (HMMs) to capture the temporal signatures of malignant and benign tissues from TeUS signals of nine patients. We model signals of benign and malignant tissues (284 and 286 signals, respectively) in their original temporal order as well as under order permutations. We then compare the resulting models using the Kullback–Liebler divergence and assess their performance differences in characterization. Moreover, we train HMMs using TeUS signals of different durations and compare their model performance when differentiating tissue types. Results : Our findings demonstrate that models of order-preserved signals perform statistically significantly better (85% accuracy) in tissue characterization compared to models of order-altered signals (62% accuracy). The performance degrades as more changes in signal order are introduced. Additionally, models trained on shorter sequences perform as accurately as models of longer sequences. Conclusion : The work presented here strongly indicates that temporal order has substantial impact on TeUS performance; thus, it plays a significant role in conveying tissue-specific information. Furthermore, shorter TeUS signals can relay sufficient information to accurately distinguish between tissue types. Significance : Understanding the impact of TeUS properties facilitates the process of its adopting in diagnostic procedures and provides insights on improving its acquisition.

Multi-parametric MRI (mp-MRI) is becoming a standard in contemporary prostate cancer screening and diagnosis, and has shown to aid physicians in cancer detection. It offers many advantages over traditional systematic biopsy, which has shown to have very high clinical false-negative rates of up to 23% at all stages of the disease. However beneficial, mp-MRI is relatively complex to interpret and suffers from inter-observer variability in lesion localization and grading. Computer-aided diagnosis (CAD) systems have been developed as a solution as they have the power to perform deterministic quantitative image analysis. We measured the accuracy of such a system validated using accurately co-registered whole-mount digitized histology. We trained a logistic linear classifier (LOGLC), support vector machine (SVC), k-nearest neighbour (KNN) and random forest classifier (RFC) in a four part ROI based experiment against: 1) cancer vs. non-cancer, 2) high-grade (Gleason score ≥4+3) vs. low-grade cancer (Gleason score

Recent studies have shown the value of Temporal Enhanced Ultrasound (TeUS) imaging for tissue characterization in transrectal ultrasound-guided prostate biopsies. Here, we present results of experiments designed to study the impact of temporal order of the data in TeUS signals. We assess the impact of variations in temporal order on the ability to automatically distinguish benign prostate-tissue from malignant tissue. We have previously used Hidden Markov Models (HMMs) to model TeUS data, as HMMs capture temporal order in time series. In the work presented here, we use HMMs to model malignant and benign tissues; the models are trained and tested on TeUS signals while introducing variation to their temporal order. We first model the signals in their original temporal order, followed by modeling the same signals under various time rearrangements. We compare the performance of these models for tissue characterization. Our results show that models trained over the original order-preserving signals perform statistically significantly better for distinguishing between malignant and benign tissues, than those trained on rearranged signals. The performance degrades as the amount of temporal-variation increases. Specifically, accuracy of tissue characterization decreases from 85% using models trained on original signals to 62% using models trained and tested on signals that are completely temporally-rearranged. These results indicate the importance of order in characterization of tissue malignancy from TeUS data.

20 Background: Multi-focality and heterogeneity in prostate cancer can confound the selection of appropriate clinical management. Our study aimed to explore radio-genomic correlations using multiparametric magnetic resonance imaging (mpMRI) against a histopathologic reference standard. Methods: Eight men with prostate cancer who underwent mpMRI followed by prostatectomy were selected for this pilot. Whole-mount histopathology was digitized and co-registered to corresponding MRI slices using a validated high-fidelity methodology.(1) Foci, including central/transitional and peripheral zone lesions were identified by a pathologist, and contoured on digitized histopathology specimens and these digitized maps were used to guide macrodissection of the individual foci for genomic copy-number aberration (CNA) analysis. Correlation of radiomics signatures with the histologic findings and genomic analysis was performed. Results: We found a broad range of CNAs revealing inter-patient and intra-prostatic heterogeneity. Recurrently-altered loci ( e.g., 8p21) containing genes of known significance ( e.g., NKX3-1) were observed. Only radiomic features derived from apparent diffusion coefficient (ADC) independently correlated with both Gleason grade (Rho=-0.62, p=0.003) and median CNA burden (Rho=-0.68, p

Radical prostatectomy is performed on approximately 40% of men with organ-confined prostate cancer. Pathologic information obtained from the prostatectomy specimen provides important prognostic information and guides recommendations for adjuvant treatment. The current pathology protocol in most centers involves primarily qualitative assessment. In this paper, we describe and evaluate our system for automatic prostate cancer detection and grading on hematoxylin & eosin-stained tissue images. Our approach is intended to address the dual challenges of large data size and the need for high-level tissue information about the locations and grades of tumors. Our system uses two stages of AdaBoost-based classification. The first provides high-level tissue component labeling of a superpixel image partitioning. The second uses the tissue component labeling to provide a classification of cancer versus noncancer, and low-grade versus high-grade cancer. We evaluated our system using 991 sub-images extracted from digital pathology images of 50 whole-mount tissue sections from 15 prostatectomy patients. We measured accuracies of 90% and 85% for the cancer versus noncancer and high-grade versus low-grade classification tasks, respectively. This system represents a first step toward automated cancer quantification on prostate digital histopathology imaging, which could pave the way for more accurately informed postprostatectomy patient care.

Recently, multi-parametric Magnetic Resonance Imaging (mp-MRI) has been used to improve the sensitivity of detecting high-risk prostate cancer (PCa). Prior to biopsy, primary and secondary cancer lesions are identified on mp-MRI. The lesions are then targeted using TRUS guidance. In this paper, for the first time, we present a fused mp-MRI-temporal-ultrasound framework for characterization of PCa, in vivo. Cancer classification results obtained using temporal ultrasound are fused with those achieved using consolidated mp-MRI maps determined by multiple observers. We verify the outcome of our study using histopathology following deformable registration of ultrasound and histology images. Fusion of temporal ultrasound and mp-MRI for characterization of the PCa results in an area under the receiver operating characteristic curve (AUC) of 0.86 for cancerous regions with Gleason scores (GSs)≥3+3, and AUC of 0.89 for those with GSs≥3+4.

Temporal ultrasound has been shown to have high classification accuracy in differentiating cancer from benign tissue. In this paper, we extend the temporal ultrasound method to classify lower grade Prostate Cancer (PCa) from all other grades. We use a group of nine patients with mostly lower grade PCa, where cancerous regions are also limited. A critical challenge is to train a classifier with limited aggressive cancerous tissue compared to low grade cancerous tissue. To resolve the problem of imbalanced data, we use Synthetic Minority Oversampling Technique (SMOTE) to generate synthetic samples for the minority class. We calculate spectral features of temporal ultrasound data and perform feature selection using Random Forests. In leave-one-patient-out cross-validation strategy, an area under receiver operating characteristic curve (AUC) of 0.74 is achieved with overall sensitivity and specificity of 70%. Using an unsupervised learning approach prior to proposed method improves sensitivity and AUC to 80% and 0.79. This work represents promising results to classify lower and higher grade PCa with limited cancerous training samples, using temporal ultrasound.

Despite recent advances in clinical oncology, prostate cancer remains a major health concern in men, where current detection techniques still lead to both over- and under-diagnosis. More accurate prediction and detection of prostate cancer can improve disease management and treatment outcome. Temporal ultrasound is a promising imaging approach that can help identify tissue-specific patterns in time-series of ultrasound data and, in turn, differentiate between benign and malignant tissues. We propose a probabilistic-temporal framework, based on hidden Markov models, for modeling ultrasound time-series data obtained from prostate cancer patients. Our results show improved prediction of malignancy compared to previously reported results, where we identify cancerous regions with over 88 % accuracy. As our models directly represent temporal aspects of the data, we expect our method to be applicable to other types of cancer in which temporal-ultrasound can be captured.

Purpose Defining prostate cancer (PCa) lesion clinical target volumes (CTVs) for multiparametric magnetic resonance imaging (mpMRI) could support focal boosting or treatment to improve outcomes or lower morbidity, necessitating appropriate CTV margins for mpMRI-defined gross tumor volumes (GTVs). This study aimed to identify CTV margins yielding 95% coverage of PCa tumors for prospective cases with high likelihood. Methods and Materials Twenty-five men with biopsy-confirmed clinical stage T1 or T2 PCa underwent pre-prostatectomy mpMRI, yielding T2-weighted, dynamic contrast-enhanced, and apparent diffusion coefficient images. Digitized whole-mount histology was contoured and registered to mpMRI scans (error ≤2 mm). Four observers contoured lesion GTVs on each mpMRI scan. CTVs were defined by isotropic and anisotropic expansion from these GTVs and from multiparametric (unioned) GTVs from 2 to 3 scans. Histologic coverage (proportions of tumor area on co-registered histology inside the CTV, measured for Gleason scores [GSs] ≥6 and ≥7) and prostate sparing (proportions of prostate volume outside the CTV) were measured. Nonparametric histologic-coverage prediction intervals defined minimal margins yielding 95% coverage for prospective cases with 78% to 92% likelihood. Results On analysis of 72 true-positive tumor detections, 95% coverage margins were 9 to 11 mm (GS ≥ 6) and 8 to 10 mm (GS ≥ 7) for single-sequence GTVs and were 8 mm (GS ≥ 6) and 6 mm (GS ≥ 7) for 3-sequence GTVs, yielding CTVs that spared 47% to 81% of prostate tissue for the majority of tumors. Inclusion of T2-weighted contours increased sparing for multiparametric CTVs with 95% coverage margins for GS ≥6, and inclusion of dynamic contrast-enhanced contours increased sparing for GS ≥7. Anisotropic 95% coverage margins increased the sparing proportions to 71% to 86%. Conclusions Multiparametric magnetic resonance imaging–defined GTVs expanded by appropriate margins may support focal boosting or treatment of PCa; however, these margins, accounting for interobserver and intertumoral variability, may preclude highly conformal CTVs. Multiparametric GTVs and anisotropic margins may reduce the required margins and improve prostate sparing.

Recent studies highlight temporal ultrasound data as highly promising in differentiating between malignant and benign tissues in prostate cancer patients. Since Hidden Markov Models can be used for capturing order and patterns in time varying signals, we employ them to model temporal aspects of ultrasound data that are typically not incorporated in existing models. By comparing order-preserving and orderaltering models, we demonstrate that the order encoded in the series is necessary to model the variability in ultrasound data of prostate tissues. In future studies, we will investigate the influence of order on the differentiation between malignant and benign tissues.

Purpose: Multiparametric magnetic resonance imaging (MPMRI) supports detection and staging of prostate cancer, but the image characteristics needed for tumor boundary delineation to support focal therapy have not been widely investigated. We quantified the detectability (image contrast between tumor and non-cancerous contralateral tissue) and the localizability (image contrast between tumor and non-cancerous neighboring tissue) of Gleason score 7 (GS7) peripheral zone (PZ) tumors on MPMRI using tumor contours mapped from histology using accurate 2D–3D registration. Methods: MPMRI [comprising T2-weighted (T2W), dynamic-contrast-enhanced (DCE), apparent diffusion coefficient (ADC) and contrast transfer coefficient images] and post-prostatectomy digitized histology images were acquired for 6 subjects. Histology contouring and grading (approved by a genitourinary pathologist) identified 7 GS7 PZ tumors. Contours were mapped to MPMRI images using semi-automated registration algorithms (combined target registration error: 2 mm). For each focus, three measurements of mean ± standard deviation of image intensity were taken on each image: tumor tissue (m T ±s T ), non-cancerous PZ tissue N ±s N ), and non-cancerous contralateral PZ tissue (m C ±s C ). Detectability [D, defined as m T -m C normalized by s T and s C added in quadrature] and localizability [L, defined as m T -m N normalized by s T and s N added in quadrature] were quantified for each focus on each image. Results: T2W images showed the strongest detectability, although detectability |D|≥1 was observed on either ADC or DCE images, or both, for all foci. Localizability on all modalities was variable; however, ADC images showed localizability |L|≥1 for 3 foci. Conclusions: Delineation of GS7 PZ tumors on individual MPMRI images faces challenges; however, images may contain complementary information, suggesting a role for fusion of information across MPMRI images for delineation.

Measurement of prostate tumour volume can inform prognosis and treatment selection, including an assessment of the suitability and feasibility of focal therapy, which can potentially spare patients the deleterious side effects of radical treatment. Prostate biopsy is the clinical standard for diagnosis but provides limited information regarding tumour volume due to sparse tissue sampling. A non-invasive means for accurate determination of tumour burden could be of clinical value and an important step toward reduction of overtreatment. Multi-parametric magnetic resonance imaging (MPMRI) is showing promise for prostate cancer diagnosis. However, the accuracy and inter-observer variability of prostate tumour volume estimation based on separate expert contouring of T2-weighted (T2W), dynamic contrastenhanced (DCE), and diffusion-weighted (DW) MRI sequences acquired using an endorectal coil at 3T is currently unknown. We investigated this question using a histologic reference standard based on a highly accurate MPMRIhistology image registration and a smooth interpolation of planimetric tumour measurements on histology. Our results showed that prostate tumour volumes estimated based on MPMRI consistently overestimated histological reference tumour volumes. The variability of tumour volume estimates across the different pulse sequences exceeded interobserver variability within any sequence. Tumour volume estimates on DCE MRI provided the lowest inter-observer variability and the highest correlation with histology tumour volumes, whereas the apparent diffusion coefficient (ADC) maps provided the lowest volume estimation error. If validated on a larger data set, the observed correlations could support the development of automated prostate tumour volume segmentation algorithms as well as correction schemes for tumour burden estimation on MPMRI.

Purpose: T2 weighted and diffusion weighted magnetic resonance imaging (MRI) show promise in isolating prostate tumours. Dynamic contrast enhanced (DCE)-MRI has also been employed as a component in multi-parametric tumour detection schemes. Model-based parameters such as K trans are conventionally used to characterize DCE images and require arterial contrast agent (CR) concentration. A robust parameter map that does not depend on arterial input may be more useful for target volume delineation. We present a dimensionless parameter (W io ) that characterizes CR wash-in and washout rates without requiring arterial CR concentration. W io is compared to K trans in terms of ability to discriminate cancer in the prostate, as demonstrated via comparison with histology. Methods: Three subjects underwent DCE-MRI using gadolinium contrast and 7 s imaging temporal resolution. A pathologist identified cancer on whole-mount histology specimens, and slides were deformably registered to MR images. The ability of W io maps to discriminate cancer was determined through receiver operating characteristic curve (ROC) analysis. Results: There is a trend that W io shows greater area under the ROC curve (AUC) than K trans with median AUC values of 0.74 and 0.69 respectively, but the difference was not statistically significant based on a Wilcoxon signed-rank test (p = 0.13). Conclusions: Preliminary results indicate that W io shows potential as a tool for K trans QA, showing similar ability to discriminate cancer in the prostate as K trans without requiring arterial CR concentration.

Determining the intra-prostatic spatial distribution and grade of prostate cancer before treatment may support improved diagnosis, therapy selection, or guidance of intra-prostatic lesion-focused therapies (e.g., radiation boosting or ablative focal therapy). Several in vivo imaging modalities are showing promise for imaging the intra-prostatic distribution of cancer. Evaluations of such imaging modalities ideally include comparisons to registered histological examinations of prostatectomy specimens, the clinically accepted “gold standard” for staging and grading prostate cancer. The registration of histology to ex vivo magnetic resonance (MR) images supports these challenging in vivo registrations by reconstructing 3D spatial information that is lost during the process of acquiring histology sections. In the work described in this chapter, ex vivo MR and histology images were acquired from nine formalin-fixed radical prostatectomy specimens which had been marked with extrinsic fiducials designed to be visible in these modalities. The histology images were registered retrospectively to the MR images using a novel algorithm based on the minimization of fiducial registration error between fiducial cross-sections on histology images and parametric fiducial curves on MR images. The 3D target registration error (TRE) was quantified based on the post-registration misalignment of manually identified homologous landmarks (3–7 per section, 184 in total), and was compared to two previously developed methods: (1) a method based on the guidance of the coarse slicing of specimens and (2) a method based on additional imaging of the images of the thick tissue slices. The proposed method yielded a mean ± standard deviation TRE of 0.71 ± 0.38 mm, 0.38–0.63 mm lower [95 % confidence interval (CI)] than the image-guided-slicing-based method, and within 0.13 mm (95 % CI) of the tissue-slice-imaging-based method. One component of the proposed method was able to refine the result from the image-guided-slicing-based method to within 0.13 mm (95 % CI) of the proposed method. The proposed method also resulted in a 70 % decrease in specimen processing time compared to the image-guided-slicing-based approach previously implemented at our center.

Accurate pathology assessment of post-prostatectomy specimens is important to determine the need for and to guide potentially life-saving adjuvant therapy. Digital pathology imaging is enabling a transition to a more objective quantification of some surgical pathology assessments, such as tumour volume, that are currently visually estimated by pathologists and subject to inter-observer variability. One challenge for tumour volume quantification is the traditional 3–5 mm spacing of images acquired from sections of radical prostatectomy specimens. Tumour volume estimates may benefit from a well-motivated approach to inter-slide tumour boundary interpolation. We implemented and tested a level set-based interpolation method and found that it produced 3D tumour surfaces that may be more biologically plausible than those produced via a simpler nearest-slide interpolation. We found that the simpler method produced larger tumour volumes, compared to the level set method, by a median factor of 2.3. For contexts where only tumour volume is of interest, we determined that the volumes produced via the simpler method can be linearly adjusted to the level setproduced volumes. The smoother surfaces from level set interpolation yielded measurable differences in tumour boundary location; this may be important in several clinical/research contexts (e.g. pathology-based imaging validation for focal therapy planning).

Imaging may enable the determination of the spatial distribution and aggressiveness of prostate cancer in vivo before treatment, possibly supporting diagnosis, therapy selection, and focal therapy guidance. 3D reconstruction of prostate histology facilitates the validation of such imaging applications. We evaluated four histology– ex vivo magnetic resonance (MR) image 3D reconstruction algorithms comprising two similarity metrics (mutual information M MI or fiducial registration error M FRE ) and two search domains (affine transformations T A or fiducial-constrained affine transformations T F ). Seven radical prostatectomy specimens were imaged with MR imaging, processed for whole-mount histology, and digitized as histology images. The algorithms were evaluated on the reconstruction error and the sensitivity of same to translational and rotational errors in initialization. Reconstruction error was quantified as the target registration error (TRE): the post-reconstruction distance between homologous point landmarks (7–15 per histology section; 132 total) identified on histology and MR images. Sensitivity to initialization was quantified using a linear model relating TRE to varied levels of translational/rotational initialization errors. The algorithm using M MI and T A yielded a mean TRE of 1.2±0.7 mm when initialized using an approach that assumes histology corresponds to the front faces of tissue blocks, but was sensitive to initialization error. The algorithm using M FRE and T A yielded a mean TRE of 0.8±0.4 mm with minimal sensitivity to initialization errors. Compared to the method used to initialize the algorithms (mean TRE 1.4±0.7 mm), a study using an algorithm with a mean TRE of 0.8 mm would require 27% fewer subjects for certain imaging validation study designs.

This paper presents the results of an in vivo clinical study to accurately characterize prostate cancer using new features of ultrasound RF time series. Methods: The mean central frequency and wavelet features of ultrasound RF time series from seven patients are used along with an elaborate framework of ultrasound to histology registration to identify and verify cancer in prostate tissue regions as small as 1.7 mm × 1.7 mm. Results: In a leave-one-patient-out cross-validation strategy, an average classification accuracy of 76% and the area under ROC curve of 0.83 are achieved using two proposed RF time series features. The results statistically significantly outperform those achieved by previously reported features in the literature. The proposed features show the clinical relevance of RF time series for in vivo characterization of cancer.

The correlation of multiple in vivo imaging modalities with graded whole-mount histology ideally requires co-registration with appropriately quantified accuracy. This work presents a framework for the co-registration of multiple in vivo prostate images with histology as a composition of pair wise registrations. This work also proposes a methodology for the measurement of the accuracy of these registrations based on a composition of target registration errors calculated from homologous landmarks in registered pairs of images. Preliminary results from the implementation of components of the framework are reported.

volume is critical in treatment planning. Whether by expert guidelines, retrograde urethrography, bony landmark, ultrasound, or CT-based planning, there is discordant anatomic information regarding the distance between apex and the bulb, also known as the genitourinary diaphragm (GUD) or membranous urethra length (MUL). In this study, we use MRI with endorectal coil in a large series of patients to better characterize the MUL in radiation therapy planning. Materials/Methods: Patients with early stage, clinically localized prostate cancer (Gleason 6 and 7, cT1c-cT2b, PSA 85% interobserver agreement within 2 mm measurement. There was no association between MUL and patient age, T-stage, Gleason score, patient height, weight, or NCCN risk group. Conclusions: Although there remains variability, the median MUL, or GUD length, is 15 mm. This report likely represents the largest evaluation of the pre-treatment MUL in men with prostate cancer treated with external beam radiation. Our data challenges traditional guidelines between the apex and bulb from smaller series and adds to critical anatomic information of male urethral length and prostate apex anatomy. Prostate MRI with endorectal coil produces a reliable MUL and should be used to optimize target volume delineation at the prostate apex. Author Disclosure: N.K. Taunk: None. O. Akin: None. X. Pei: None. M. Zelefsky: None.

Purpose: Evaluation ofin vivo prostate imaging modalities for determining the spatial distribution and aggressiveness of prostate cancer ideally requires accurate registration of images to an accepted reference standard, such as histopathological examination of radical prostatectomy specimens. Three-dimensional (3D) reconstruction of prostate histology facilitates these registration-based evaluations by reintroducing 3D spatial information lost during histology processing. Because the reconstruction accuracy may constrain the clinical questions that can be answered with these data, it is important to assess the tradeoffs between minimally disruptive methods based on intrinsic image information and potentially more robust methods based on extrinsic fiducial markers. Methods: Ex vivo magnetic resonance (MR) images and digitized whole-mount histology images from 12 radical prostatectomy specimens were used to evaluate four 3D histology reconstruction algorithms. 3D reconstructions were computed by registering each histology image to the corresponding ex vivo MR image using one of two similarity metrics (mutual information or fiducial registration error) and one of two search domains (affine transformations or a constrained subset thereof). The algorithms were evaluated for accuracy using the mean target registration error (TRE) computed from homologous intrinsic point landmarks (3–16 per histology section; 232 total) identified on histology and MR images, and for the sensitivity of TRE to rotational, translational, and scaling initialization errors. Results: The algorithms using fiducial registration error and mutual information had mean ± standard deviation TREs of 0.7 ± 0.4 and 1.2 ± 0.7 mm, respectively, and one algorithm using fiducial registration error and affine transforms had negligible sensitivities to initialization errors. The postoptimization values of the mutual information-based metric showed evidence of errors due to both the optimizer and the similarity metric, and variation of parameters of the mutual information-based metric did not improve its performance. Conclusions: The extrinsic fiducial-based algorithm had lower mean TRE and lower sensitivity to initialization than the intrinsic intensity-based algorithm using mutual information. A model relating statistical power to registration error for certain imaging validation study designs estimated that a reconstruction algorithm with a mean TRE of 0.7 mm would require 27% fewer subjects than the method used to initialize the algorithms (mean TRE 1.3 ± 0.7 mm), suggesting the choice of reconstruction technique can have a substantial impact on the design of imaging validation studies, and on their overall cost.