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1. Evaluation of Interventional Planning Software Features for MR-guided Transrectal Prostate Biopsies

   https://doi.org/10.1109/BIBE50027.2020.00161  

   Velazco-Garcia, Jose D.; Navkar, Nikhil V.; Balakrishnan, Shidin; Abinahed, Julien; Al-Ansari, Abdulla; Darweesh, Adham; Al-Rumaihi, Khalid; Christoforou, Eftychios G.; Leiss, Ernst L.; Karkoub, Mansour; et al (October 2020, IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE))

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   This work presents an interventional planning software to be used in conjunction with a robotic manipulator to perform transrectal MR guided prostate biopsies. The interventional software was designed taking in consideration a generic manipulator used under the two modes of operation: side-firing and end-firing of the biopsy needle. Studies were conducted with urologists using the software to plan virtual biopsies. The results show features of software relevant for operating efficiently under the two modes of operation. 

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2. Preliminary Evaluation of Robotic Transrectal Biopsy System on an Interventional Planning Software

   https://doi.org/10.1109/BIBE.2019.00070  

   Velazco-Garcia, Jose D.; Leiss, Ernst L.; Karkoub, Mansour; Tsiamyrtzis, Panagiotis; Tsekos, Nikolaos V.; Navkar, Nikhil V.; Balakrishnan, Shidin; Abinahed, Julien; Al-Ansari, Abdulla; Younes, Georges; et al (October 2019, 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE))

   Prostate biopsy is considered as a definitive way for diagnosing prostate malignancies. Urologists are currently moving towards MR-guided prostate biopsies over conventional transrectal ultrasound-guided biopsies for prostate cancer detection. Recently, robotic systems have started to emerge as an assistance tool for urologists to perform MR-guided prostate biopsies. However, these robotic assistance systems are designed for a specific clinical environment and cannot be adapted to modifications or changes applied to the clinical setting and/or workflow. This work presents the preliminary design of a cable-driven manipulator developed to be used in both MR scanners and MR-ultrasound fusion systems. The proposed manipulator design and functionality are evaluated on a simulated virtual environment. The simulation is created on an in-house developed interventional planning software to evaluate the ergonomics and usability. The results show that urologists can benefit from the proposed design of the manipulator and planning software to accurately perform biopsies of targeted areas in the prostate. 

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3. Prostate Cancer Risk Stratification via Nondestructive 3D Pathology with Deep Learning–Assisted Gland Analysis

   https://doi.org/10.1158/0008-5472.CAN-21-2843  

   Xie, Weisi; Reder, Nicholas P.; Koyuncu, Can; Leo, Patrick; Hawley, Sarah; Huang, Hongyi; Mao, Chenyi; Postupna, Nadia; Kang, Soyoung; Serafin, Robert; et al (December 2021, Cancer Research)

   Abstract Prostate cancer treatment planning is largely dependent upon examination of core-needle biopsies. The microscopic architecture of the prostate glands forms the basis for prognostic grading by pathologists. Interpretation of these convoluted three-dimensional (3D) glandular structures via visual inspection of a limited number of two-dimensional (2D) histology sections is often unreliable, which contributes to the under- and overtreatment of patients. To improve risk assessment and treatment decisions, we have developed a workflow for nondestructive 3D pathology and computational analysis of whole prostate biopsies labeled with a rapid and inexpensive fluorescent analogue of standard hematoxylin and eosin (H&E) staining. This analysis is based on interpretable glandular features and is facilitated by the development of image translation–assisted segmentation in 3D (ITAS3D). ITAS3D is a generalizable deep learning–based strategy that enables tissue microstructures to be volumetrically segmented in an annotation-free and objective (biomarker-based) manner without requiring immunolabeling. As a preliminary demonstration of the translational value of a computational 3D versus a computational 2D pathology approach, we imaged 300 ex vivo biopsies extracted from 50 archived radical prostatectomy specimens, of which, 118 biopsies contained cancer. The 3D glandular features in cancer biopsies were superior to corresponding 2D features for risk stratification of patients with low- to intermediate-risk prostate cancer based on their clinical biochemical recurrence outcomes. The results of this study support the use of computational 3D pathology for guiding the clinical management of prostate cancer. Significance:An end-to-end pipeline for deep learning–assisted computational 3D histology analysis of whole prostate biopsies shows that nondestructive 3D pathology has the potential to enable superior prognostic stratification of patients with prostate cancer. 

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4. Nondestructive 3D pathology with analysis of nuclear features for prostate cancer risk assessment

   https://doi.org/10.1002/path.6090  

   Serafin, Robert; Koyuncu, Can; Xie, Weisi; Huang, Hongyi; Glaser, Adam_K; Reder, Nicholas_P; Janowczyk, Andrew; True, Lawrence_D; Madabhushi, Anant; Liu, Jonathan_TC (May 2023, The Journal of Pathology)

   Abstract Prostate cancer treatment decisions rely heavily on subjective visual interpretation [assigning Gleason patterns or International Society of Urological Pathology (ISUP) grade groups] of limited numbers of two‐dimensional (2D) histology sections. Under this paradigm, interobserver variance is high, with ISUP grades not correlating well with outcome for individual patients, and this contributes to the over‐ and undertreatment of patients. Recent studies have demonstrated improved prognostication of prostate cancer outcomes based on computational analyses of glands and nuclei within 2D whole slide images. Our group has also shown that the computational analysis of three‐dimensional (3D) glandular features, extracted from 3D pathology datasets of whole intact biopsies, can allow for improved recurrence prediction compared to corresponding 2D features. Here we seek to expand on these prior studies by exploring the prognostic value of 3D shape‐based nuclear features in prostate cancer (e.g. nuclear size, sphericity). 3D pathology datasets were generated using open‐top light‐sheet (OTLS) microscopy of 102 cancer‐containing biopsies extractedex vivofrom the prostatectomy specimens of 46 patients. A deep learning‐based workflow was developed for 3D nuclear segmentation within the glandular epithelium versus stromal regions of the biopsies. 3D shape‐based nuclear features were extracted, and a nested cross‐validation scheme was used to train a supervised machine classifier based on 5‐year biochemical recurrence (BCR) outcomes. Nuclear features of the glandular epithelium were found to be more prognostic than stromal cell nuclear features (area under the ROC curve [AUC] = 0.72 versus 0.63). 3D shape‐based nuclear features of the glandular epithelium were also more strongly associated with the risk of BCR than analogous 2D features (AUC = 0.72 versus 0.62). The results of this preliminary investigation suggest that 3D shape‐based nuclear features are associated with prostate cancer aggressiveness and could be of value for the development of decision‐support tools. © 2023 The Pathological Society of Great Britain and Ireland. 

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5. Post-procedure micro-CT analyses of coronary artery stenting in left main vessels of reanimated and perfusion-fixed human hearts

   https://doi.org/10.1186/s12938-023-01090-2  

   Valenzuela, Thomas F.; Iaizzo, Paul A. (December 2023, BioMedical Engineering OnLine)

   Abstract BackgroundPercutaneous coronary interventions (PCIs) within left main coronary arteries are high-risk procedures that require optimization of interactions between stent(s) and diseased vessels. Optical Coherence Tomography (OCT) is a widely accepted tool that enhances physicians’ ability to assess proper stent appositions during clinical procedures. The primary aim of this study was to develop complementary post-procedure imaging methodologies to better assess and interpret outcomes of left main PCI procedures, utilizing both reanimated and perfusion-fixed human hearts. MethodsPCIs were performed while obtaining OCT scans within the left main anatomies of six human hearts. Subsequently, each heart was scanned with a micro-CT scanner with optimized parameters to achieve resolutions up to 20 µm. Scans were reconstructed and imported into a DICOM segmentation software to generate computational models of implanted stents and associated coronary vessels. 2D images from OCT that were obtained during PCIs were compared to the 3D models generated from micro-CT reconstructions. In addition, the 3D models were utilized to create virtual reality scenes and enlarged 3D prints for development of “mixed reality” tools relative to bifurcation stenting within human left main coronary arteries. ResultsWe developed reproducible methodologies for post-implant analyses of coronary artery stenting procedures. In addition, we generated high-resolution 3D computational models, with ~ 20-micron resolutions, of PCIs performed within reanimated and perfusion-fixed heart specimens. ConclusionsGenerated computational models of left main PCIs performed in isolated human hearts can be used to obtain detailed measurements that provide further clinical insights on procedural outcomes. The 3D models from these procedures are useful for generating virtual reality scenes and 3D prints for physician training and education. 

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