Prostate cancer classification using radiomics and machine learning on mp-MRI validated using co-registered histology.

Background: Multi-parametric magnetic resonance imaging (mp-MRI) is emerging as a useful tool for prostate cancer (PCa) detection but currently has unaddressed limitations. Computer aided diagnosis (CAD) systems have been developed to address these needs, but many approaches used to generate and validate the models have inherent biases.

Method: All clinically significant PCa on histology was mapped to mp-MRI using a previously validated registration algorithm.

Short-duration dynamic [F]DCFPyL PET and CT perfusion imaging to localize dominant intraprostatic lesions in prostate cancer: validation against digital histopathology and comparison to [F]DCFPyL PET/MR at 120 minutes.

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 [F]DCFPyL PET and CT perfusion (CTP) for detecting and localizing DIL against digital histopathological images.

Prostate specific membrane antigen positron emission tomography for lesion-directed high-dose-rate brachytherapy dose escalation.

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.

Correction to: Stochastic Sequential Modeling: Toward Improved Prostate Cancer Diagnosis Through Temporal-Ultrasound.

The authors have noted an omission in the original acknowledgements. The correct acknowledgements are as follows: Acknowledgements: This work was partially supported by Grants from NSERC Discovery to Hagit Shatkay and Parvin Mousavi, NSERC and CIHR CHRP to Parvin Mousavi and NIH R01 LM012527, NIH U54 GM104941, NSF IIS EAGER #1650851 & NSF HDR #1940080 to Hagit Shatkay.

Histologic validation of auto-contoured dominant intraprostatic lesions on [F] DCFPyL PSMA-PET imaging.

Background: PSMA-PET has shown good concordance with histology, but there is a need to investigate the ability of PSMA-PET to delineate DIL boundaries for guided biopsy and focal therapy planning.

Objective: To determine threshold and margin combinations that satisfy the following criteria: ≥95% sensitivity with max specificity and ≥95% specificity with max sensitivity.

Design, Setting And Participants: We registered pathologist-annotated whole-mount mid-gland prostatectomy histology sections cut in 4.

Stochastic Sequential Modeling: Toward Improved Prostate Cancer Diagnosis Through Temporal-Ultrasound.

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.

Automatic cancer detection on digital histopathology images of mid-gland radical prostatectomy specimens.

Automatic cancer detection on radical prostatectomy (RP) sections facilitates graphical and quantitative surgical pathology reporting, which can potentially benefit postsurgery follow-up care and treatment planning. It can also support imaging validation studies using a histologic reference standard and pathology research studies. This problem is challenging due to the large sizes of digital histopathology whole-mount whole-slide images (WSIs) of RP sections and staining variability across different WSIs.

Histologic tissue components provide major cues for machine learning-based prostate cancer detection and grading on prostatectomy specimens.

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.

Characterization of clinical human prostate cancer lesions using 3.0-T sodium MRI registered to Gleason-graded whole-mount histopathology.

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 T -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.

Stochastic Modeling of Temporal Enhanced Ultrasound: Impact of Temporal Properties on Prostate Cancer Characterization.

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.

[F]-DCFPyL Positron Emission Tomography/Magnetic Resonance Imaging for Localization of Dominant Intraprostatic Foci: First Experience.

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.

Toward Prostate Cancer Contouring Guidelines on Magnetic Resonance Imaging: Dominant Lesion Gross and Clinical Target Volume Coverage Via Accurate Histology Fusion.

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.

Computer-Aided Prostate Cancer Detection Using Ultrasound RF Time Series: In Vivo Feasibility Study.

Unlabelled: This paper presents the results of a computer-aided intervention solution to demonstrate the application of RF time series for characterization of prostate cancer, in vivo.

Methods: We pre-process RF time series features extracted from 14 patients using hierarchical clustering to remove possible outliers. Then, we demonstrate that the mean central frequency and wavelet features extracted from a group of patients can be used to build a nonlinear classifier which can be applied successfully to differentiate between cancerous and normal tissue regions of an unseen patient.

Ultrasound-Based Characterization of Prostate Cancer Using Joint Independent Component Analysis.

Objective: This paper presents the results of a new approach for selection of RF time series features based on joint independent component analysis for in vivo characterization of prostate cancer.

Methods: We project three sets of RF time series features extracted from the spectrum, fractal dimension, and the wavelet transform of the ultrasound RF data on a space spanned by five joint independent components. Then, we demonstrate that the obtained mixing coefficients from a group of patients can be used to train a classifier, which can be applied to characterize cancerous regions of a test patient.

Ultrasound-based characterization of prostate cancer: an in vivo clinical feasibility study.

Unlabelled: 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 x 1.

3D prostate histology image reconstruction: Quantifying the impact of tissue deformation and histology section location.

Background: Guidelines for localizing prostate cancer on imaging are ideally informed by registered post-prostatectomy histology. 3D histology reconstruction methods can support this by reintroducing 3D spatial information lost during histology processing. The need to register small, high-grade foci drives a need for high accuracy.

Prostate histopathology: learning tissue component histograms for cancer detection and classification.

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.

Registration of prostate histology images to ex vivo MR images via strand-shaped fiducials.

Purpose: To present and evaluate a method for registration of whole-mount prostate digital histology images to ex vivo magnetic resonance (MR) images.

Materials And Methods: Nine radical prostatectomy specimens were marked with 10 strand-shaped fiducial markers per specimen, imaged with T1- and T2-weighted 3T MRI protocols, sliced at 4.4-mm intervals, processed for whole-mount histology, and the resulting histological sections (3-5 per specimen, 34 in total) were digitized.