Jose A. Gomez

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. 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.

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

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

Prostate: Registration of Digital Histopathologic Images to in Vivo MR Images Acquired by Using Endorectal Receive Coil

PURPOSE To develop and evaluate a technique for the registration of in vivo prostate magnetic resonance (MR) images to digital histopathologic images by using image-guided specimen slicing based on strand-shaped fiducial markers relating specimen imaging to histopathologic examination. MATERIALS AND METHODS The study was approved by the institutional review board (the University of Western Ontario Health Sciences Research Ethics Board, London, Ontario, Canada), and written informed consent was obtained from all patients. This work proposed and evaluated a technique utilizing developed fiducial markers and real-time three-dimensional visualization in support of image guidance for ex vivo prostate specimen slicing parallel to the MR imaging planes prior to digitization, simplifying the registration process. Means, standard deviations, root-mean-square errors, and 95% confidence intervals are reported for all evaluated measurements. RESULTS The slicing error was within the 2.2 mm thickness of the diagnostic-quality MR imaging sections, with a tissue block thickness standard deviation of 0.2 mm. Rigid registration provided negligible postregistration overlap of the smallest clinically important tumors (0.2 cm(3)) at histologic examination and MR imaging, whereas the tested nonrigid registration method yielded a mean target registration error of 1.1 mm and provided useful coregistration of such tumors. CONCLUSION This method for the registration of prostate digital histopathologic images to in vivo MR images acquired by using an endorectal receive coil was sufficiently accurate for coregistering the smallest clinically important lesions with 95% confidence.

Pleomorphic and dedifferentiated leiomyosarcoma: clinicopathologic and immunohistochemical study of 41 cases

In this article, we supplement the few published articles by describing the clinical and pathologic features of pleomorphic and dedifferentiated leiomyosarcoma from 41 patients (27 women and 14 men) with an age range of 25 to 75 years (mean, 56.5 years), representing the largest cohort reported to date. The typical leiomyosarcoma component accounted for <5% to 60% (mean, 15%) of the tumor. The pleomorphic sarcoma component was composed of polygonal cells in 57% of cases, spindle cells in 21%, a combination of polygonal, epithelioid, rhabdoid, and/or spindle cells in 18%, and predominantly epithelioid cells in 3%. The classical leiomyosarcoma component was positive for at least one myogenic immunohistochemical marker in 29 tumors tested; smooth muscle actin in 100% (27/27), calponin in 90% (9/10), muscle-specific actin in 90% (10/11), desmin in 86% (23/27), smooth muscle myosin heavy chain (SMMS-1) in 67% (4/6), and caldesmon in 57% (4/7). The pleomorphic sarcoma component was reactive for at least one muscle marker in 77% (23/30) of cases; smooth muscle actin in 63% (17/27), calponin in 60% (6/10), SMMS-1 in 60% (3/5), desmin in 59% (16/27), muscle-specific actin in 40% (4/10), and caldesmon in 29% (2/7). The classical leiomyosarcoma component was often strongly positive for myogenic markers, and the pleomorphic sarcoma component usually showed focal and less intense immunoreactivity. Based on staining for muscle markers in the pleomorphic component, twenty-three cases were designated as pleomorphic leiomyosarcoma, and 7 cases were designated as dedifferentiated leiomyosarcoma (negative for all muscle markers used). Eleven cases, in which tissue was not available for immunhistochemical stains, the question of pleomorphic versus dedifferentiated leiomyosarcoma could not be answered. The incidence of metastasis was 89% (32/36) and the mortality rate was 50% (18/36) at last follow-up (3-104 months; mean, 27.5 months).

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. Accurate 3D reconstruction method design is impacted by the answers to the following central questions of this work. (1) How does prostate tissue deform during histology processing? (2) What spatial misalignment of the tissue sections is induced by microtome cutting? (3) How does the choice of reconstruction model affect histology reconstruction accuracy? Materials and Methods: Histology, paraffin block face and magnetic resonance images were acquired for 18 whole mid-gland tissue slices from six prostates. 7-15 homologous landmarks were identified on each image. Tissue deformation due to histology processing was characterized using the target registration error (TRE) after landmark-based registration under four deformation models (rigid, similarity, affine and thin-plate-spline [TPS]). The misalignment of histology sections from the front faces of tissue slices was quantified using manually identified landmarks. The impact of reconstruction models on the TRE after landmark-based reconstruction was measured under eight reconstruction models comprising one of four deformation models with and without constraining histology images to the tissue slice front faces. Results: Isotropic scaling improved the mean TRE by 0.8-1.0 mm (all results reported as 95% confidence intervals), while skew or TPS deformation improved the mean TRE by <0.1 mm. The mean misalignment was 1.1-1.9° (angle) and 0.9-1.3 mm (depth). Using isotropic scaling, the front face constraint raised the mean TRE by 0.6-0.8 mm. Conclusions: For sub-millimeter accuracy, 3D reconstruction models should not constrain histology images to the tissue slice front faces and should be flexible enough to model isotropic scaling.

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

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. Results: In a cross-validation strategy, we show an average area under receiver operating characteristic curve (AUC) of 0.93 and classification accuracy of 80%. To validate our results, we present a detailed ultrasound to histology registration framework. Conclusion: Ultrasound RF time series results in differentiation of cancerous and normal tissue with high AUC.

Registration of in vivo prostate magnetic resonance images to digital histopathology images

Early and accurate diagnosis of prostate cancer enables minimally invasive therapies to cure the cancer with less morbidity. The purpose of this work is to non-rigidly register in vivo pre-prostatectomy prostate medical images to regionally-graded histopathology images from post-prostatectomy specimens, seeking a relationship between the multi parametric imaging and cancer distribution and aggressiveness. Our approach uses image-based registration in combination with a magnetically tracked probe to orient the physical slicing of the specimen to be parallel to the in vivo imaging planes, yielding a tractable 2D registration problem. We measured a target registration error of 0.85 mm, a mean slicing plane marking error of 0.7 mm, and a mean slicing error of 0.6 mm; these results compare favourably with our 2.2 mm diagnostic MR image thickness. Qualitative evaluation of in vivo imaging-histopathology fusion reveals excellent anatomic concordance between MR and digital histopathology.

3D prostate histology reconstruction: An evaluation of image-based and fiducial-based algorithms

PURPOSE Evaluation of in 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.

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. Results: In a leave-one-patient-out cross validation, an area under receiver operating characteristic curve of 0.93 and classification accuracy of 84% are achieved. Conclusion: Ultrasound RF time series can be used to accurately characterize prostate cancer, in vivo without the need for exhaustive search in the feature space. Significance: We use joint independent component analysis for systematic fusion of multiple sets of RF time series features, within a machine learning framework, to characterize PCa in an in vivo study.

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 1.7 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.