Jack Zeineh

Gland ring morphometry for prostate cancer prognosis in multispectral immunofluorescence images.

Morphometric features characterizing the fusion and fragmentation of the glandular architecture of advanced prostate cancer have not previously been based upon the automated segmentation of discrete gland rings, due in part to the difficulty of extracting these structures from the H&E stained tissues. We present a novel approach for segmenting gland rings in multi-spectral immunofluorescence (IF) images and demonstrate the utility of the resultant features in predicting cancer recurrence in a cohort of 1956 images of prostate biopsies and prostatectomies from 679 patients. The proposed approach is evaluated for prediction of actual clinical outcomes of interest to physicians in comparison with previously published gland-unit features, yielding a concordance index (CI) of 0.67. This compares favorably to the CI of 0.66 obtained using a semi-automated segmentation of the corresponding H&E images from the same patients. This work presents the first algorithms for segmentation of gland rings lacking a central lumen, and for separation of touching epithelial units, and introduces new gland adjacency features for predicting prostate cancer clinical progression across both biopsy and prostatectomy images.

Iterative approach to joint segmentation of cellular structures

Accurate segmentation of overlapping nuclei is essential in determining nuclei count and evaluating the sub-cellular localization of protein biomarkers in image Cytometry and Histology. Current cellular segmentation algorithms generally lack fast and reliable methods for disambiguating clumped nuclei. In immuno-fluorescence segmentation, solutions to challenges including nuclei misclassification, irregular boundaries, and under-segmentation require reliable separation of clumped nuclei. This paper presents a fast and accurate algorithm for joint segmentation of cellular cytoplasm and nuclei incorporating procedures for reliably separating overlapping nuclei. The algorithm utilizes a combination of ideas and is a significant improvement on state-of-the-art algorithms for this application. First, an adaptive process that includes top-hat filtering, blob detection and distance transforms estimates the inverse illumination field and corrects for intensity non-uniformity. Minimum-error-thresholding based binarization augmented by statistical stability estimation is applied prior to seed-detection constrained by a distance-map-based scale-selection to identify candidate seeds for nuclei segmentation. The nuclei clustering step also incorporates error estimation based on statistical stability. This enables the algorithm to perform localized error correction. Final steps include artifact removal and reclassification of nuclei objects near the cytoplasm boundary as epithelial or stroma. Evaluation using 48 realistic phantom images with known ground-truth shows overall segmentation accuracy exceeding 96%. It significantly outperformed two state-of-the-art algorithms in clumped nuclei separation. Tests on 926 prostate biopsy images (326 patients) show that the segmentation improvement improves the predictive power of nuclei architecture features based on the minimum spanning tree algorithm. The algorithm has been deployed in a large scale pathology application.

Ensemble Network for Region Identification in Breast Histopathology Slides.

Accurate analysis of tissue structures in breast cancer histopathology slides is crucial for staging treatments and predicting outcome. Such analysis depends on identification of tissue architecture in different regions, and determining the different types of cancer morphology which includes in-situ carcinoma, invasive tumor, and benign tumor. We propose an automated classification method for identifying these micro-architectures using an ensemble of convolutional neural networks. This ensemble is constructed by combining multiple networks, trained using different data subset sampling and image perturbation models. Our proposed approach results in a high performing detector with robustness to data variations.

Development and validation of a novel automated Gleason grade and molecular profile that define a highly predictive prostate cancer progression algorithm-based test

BackgroundPostoperative risk assessment remains an important variable in the effective treatment of prostate cancer. There is an unmet clinical need for a test with the potential to enhance the Gleason grading system with novel features that more accurately reflect a personalized prediction of clinical failure.MethodsA prospectively designed retrospective study utilizing 892 patients, post radical prostatectomy, followed for a median of 8 years. In training, using digital image analysis to combine microscopic pattern analysis/machine learning with biomarkers, we evaluated Precise Post-op model results to predict clinical failure in 446 patients. The derived prognostic score was validated in 446 patients. Eligible subjects required complete clinical-pathologic variables and were excluded if they had received neoadjuvant treatment including androgen deprivation, radiation or chemotherapy prior to surgery. No patients were enrolled with metastatic disease prior to surgery. Evaluate the assay using time to event concordance index (C-index), Kaplan–Meier, and hazards ratio.ResultsIn the training cohort (n = 306), the Precise Post-op test predicted significant clinical failure with a C-index of 0.82, [95% CI: 0.76–0.86], HR:6.7, [95% CI: 3.59–12.45], p < 0.00001. Results were confirmed in validation (n = 284) with a C-index 0.77 [95% CI: 0.72–0.81], HR = 5.4, [95% CI: 2.74–10.52], p < 0.00001. By comparison, a clinical feature base model had a C-index of 0.70 with a HR = 3.7. The Post-Op test also re-classified 58% of CAPRA-S intermediate risk patients as low risk for clinical failure.ConclusionsPrecise Post-op tissue-based test discriminates low from intermediate high risk prostate cancer disease progression in the postoperative setting. Guided by machine learning, the test enhances traditional Gleason grading with novel features that accurately reflect the biology of personalized risk assignment.

Abstract B093: Development and validation of a novel automated Gleason grade and molecular profile that define a highly predictive prostate cancer progression algorithm-based test

Background: Postoperative risk assessment remains an important variable in the treatment of prostate cancer. We aimed to develop and validate a tissue-based on-slide prognostic risk model test to identify which patients would benefit most from early intervention. Methods: Prospectively designed retrospective study utilizing 892 patients, post radical prostatectomy, followed for a median of 8 years. We constructed a matched 50:50 training and test cohort to identify compound features using protein multiplex immunofluorescence combined with novel morphometry generated by advanced image analysis and machine learning tools. The primary endpoint was the development of a postoperative clinical failure (CF) including PSA rise post-adjuvant treatment, or metastasis or prostate cancer specific mortality. Time to event C-index and Kaplan-Meier were used to assess accuracy. Results: The Precise postop model in training (n=306) selected 4 compound imaging features and one clinical variable to predict CF with a performance C-index of 0.82, [95%CI: 0.76-0.86], HR:6.7, [95%CI: 3.59-12.45], p Conclusion: Patients with high Precise postop scores had a higher likelihood of having disease progression within 8 years with a reduced durable response to androgen-deprivation therapy. The Precise postop algorithm-based model guided by machine learning replaces subjective Gleason grading with novel image features that combine morphometry with biologic attributes that more accurately reflect disease potential. Citation Format: Michael Joseph Donovan, Gerardo Fernandez, Richard Scott, Jack Zeineh, Giovanni Koll, Faisal Khan, Nataliya Gladoun, Elizabeth Charytonowicz, Ash Tewari, Carlos Cordon-Cardo. Development and validation of a novel automated Gleason grade and molecular profile that define a highly predictive prostate cancer progression algorithm-based test [abstract]. In: Proceedings of the AACR Special Conference: Prostate Cancer: Advances in Basic, Translational, and Clinical Research; 2017 Dec 2-5; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(16 Suppl):Abstract nr B093.

MACHINE LEARNING-BASED HISTOPATHOLOGICAL APPROACH TO TAU PATHOLOGY

Background:Creutzfeldt-Jakob disease (CJD) is a rapidly progressive dementia with an illness duration averaging approximately 46 months. There are several challenges to conducting research and surveillance activities on CJD, including its rapid progression, difficulty to diagnosis, geographic dispersal, and rarity. Several studies have been conducted examining the utility of using teleneurology for research purposes of neurologic diseases. We conducted a feasibility study of using teleneurology for research and surveillance purposes inCJD.Methods:Subjectswere included in the study if theymet criteria for probable sporadic CJD (sCJD) or had a positive real-time quaking induced conversion (RT-QuIC) result. Subjects and a research partner were given a choice of in-person visit, teleneurology visit, or medical record review only. A standardized history and examination were collected as well as standardized instruments to measure cognition (Telephone Interview for Cognitive Status, TICS), functional status (MRC Prion Disease Rating Scale), and neuropsychiatric symptoms (Neuropsychiatric Inventory Questionnaire, NPI-Q). For teleneurology visits, subjects participated in the evaluation using secure software (Cisco Jabber) that could be installed on any internet connected device with a camera of their choice. Subjects were followed longitudinally on a monthly or bimonthly basis. Results:Over a 10-month period, the study received 81 referrals from 27 states. All but two enrolled subjects (95%) chose the teleneurology arm of the study and the majority participated from their homes (88%). Subjects and study partners expressed ease of use, convenience, and likelihood of recommending the teleneurology modality over other research modalities to potential participants. The quality of the examination depended on subject cooperation aswell as internet connectivity. All but one participant proceeded to autopsy (95% autopsy rate). Conclusions: This study demonstrates the feasibility of conducting research and surveillance activities of CJD subjects using teleneurology. Subjects preferred this modality, felt comfortable with its use, and may have been more likely to proceed to autopsy given involvement in the study. Further research should study the validity and reliability of instruments used to study this population remotely.

Pathological Gleason prediction through gland ring morphometry in immunofluorescent prostate cancer images

The Gleason score is the most common architectural and morphological assessment of prostate cancer severity and prognosis. There have been numerous quantitative techniques developed to approximate and duplicate the Gleason scoring system. Most of these approaches have been developed in standard H and E brightfield microscopy. Immunofluorescence (IF) image analysis of tissue pathology has recently been proven to be extremely valuable and robust in developing prognostic assessments of disease, particularly in prostate cancer. There have been significant advances in the literature in quantitative biomarker expression as well as characterization of glandular architectures in discrete gland rings. In this work we leverage a new method of segmenting gland rings in IF images for predicting the pathological Gleason; both the clinical and the image specific grade, which may not necessarily be the same. We combine these measures with nuclear specific characteristics as assessed by the MST algorithm. Our individual features correlate well univariately with the Gleason grades, and in a multivariate setting have an accuracy of 85% in predicting the Gleason grade. Additionally, these features correlate strongly with clinical progression outcomes (CI of 0.89), significantly outperforming the clinical Gleason grades (CI of 0.78). This work presents the first assessment of morphological gland unit features from IF images for predicting the Gleason grade.

Integration of co-localized glandular morphometry and protein biomarker expression in immunofluorescent images for prostate cancer prognosis

Immunofluorescent (IF) image analysis of tissue pathology has proven to be extremely valuable and robust in developing prognostic assessments of disease, particularly in prostate cancer. There have been significant advances in the literature in quantitative biomarker expression as well as characterization of glandular architectures in discrete gland rings. However, while biomarker and glandular morphometric features have been combined as separate predictors in multivariate models, there is a lack of integrative features for biomarkers co-localized within specific morphological sub-types; for example the evaluation of androgen receptor (AR) expression within Gleason 3 glands only. In this work we propose a novel framework employing multiple techniques to generate integrated metrics of morphology and biomarker expression. We demonstrate the utility of the approaches in predicting clinical disease progression in images from 326 prostate biopsies and 373 prostatectomies. Our proposed integrative approaches yield significant improvements over existing IF image feature metrics. This work presents some of the first algorithms for generating innovative characteristics in tissue diagnostics that integrate co-localized morphometry and protein biomarker expression.