Abstract-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.

The proposed method registers digital histology to prostate MR images, yielding 70% reduced processing time and mean accuracy sufficient to achieve 85% overlap on histology and ex vivo MR images for a 0.2 cc spherical tumor.

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.

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 crossvalidation 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 highergrade 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. The most used treatment for prostate-cancer (PCa) that is organ-confined is radical prostatectomy (RP), the removal of the prostate gland. Approximately 40% of prostate cancer patients undergo this surgery each year in the United States 1. Serum prostate-specific antigen (PSA) relapse occurs in 17%-29% of patients, reflecting cancer recurrence 2,3. Post-surgery prognosis, recurrence prediction, and selection and guidance for adjuvant therapy are all informed by the surgical pathology report. Typical pathology reports include tumor size, location, spread, and aggressiveness levels. In addition, PCa patients are grouped based on the Gleason score (GS), which is computed as the sum of the primary and secondary Gleason grades 3 at RP, into grade group 1 (GS 6; G3 + 3), grade group 2 (GS 7; G3 + 4), grade group 3 (GS7; G4 + 3), grade group 4 (GS 8; G4 + 4) and grade group 5 (GS 9-10; G4 + 5, G5 + 4, and G5 + 5) disease 4,5 , with treatment determined according to the risk level 6. Thus, although accurate post-RP risk stratification is crucial, currently, clinical pathology reporting is primarily qualitative and subject to intra-and inter-observer variability. This leads to challenges for quantitative and repeatable pathology reporting and interpretation regarding the lesion size, location, spread, and Gleason grade or score 3,7-10 .

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Ultrasound RF time series results in differentiation of cancerous and normal tissue with high AUC.

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