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1. 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)

   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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2. End‐user evaluation of software‐generated intervention planning environment for transrectal magnetic resonance‐guided prostate biopsies

   https://doi.org/10.1002/rcs.2179  

   Velazco‐Garcia, Jose D. ; Navkar, Nikhil V. ; Balakrishnan, Shidin ; Abi‐Nahed, Julien ; Al‐Rumaihi, Khalid ; Darweesh, Adham ; Al‐Ansari, Abdulla ; Christoforou, Eftychios G. ; Karkoub, Mansour ; Leiss, Ernst L. ; et al ( October 2020 , The International Journal of Medical Robotics and Computer Assisted Surgery)

   This study presents user evaluation studies to assess the effect of information rendered by an interventional planning software on the operator's ability to plan transrectal magnetic resonance (MR)‐guided prostate biopsies using actuated robotic manipulators.

   An intervention planning software was developed based on the clinical workflow followed for MR‐guided transrectal prostate biopsies. The software was designed to interface with a generic virtual manipulator and simulate an intervention environment using 2D and 3D scenes. User studies were conducted with urologists using the developed software to plan virtual biopsies.

   User studies demonstrated that urologists with prior experience in using 3D software completed the planning less time. 3D scenes were required to control all degrees‐of‐freedom of the manipulator, while 2D scenes were sufficient for planar motion of the manipulator.

   The study provides insights on using 2D versus 3D environment from a urologist's perspective for different operational modes of MR‐guided prostate biopsy systems.

    

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3. Visual Assessment of 2-Dimensional Levels Within 3-Dimensional Pathology Data Sets of Prostate Needle Biopsies Reveals Substantial Spatial Heterogeneity

   https://doi.org/10.1016/j.labinv.2023.100265  

   Koyuncu, Can ; Janowczyk, Andrew ; Farre, Xavier ; Pathak, Tilak ; Mirtti, Tuomas ; Fernandez, Pedro L. ; Pons, Laura ; Reder, Nicholas P. ; Serafin, Robert ; Chow, Sarah S.L. ; et al ( December 2023 , Laboratory Investigation)

   Prostate cancer prognostication largely relies on visual assessment of a few thinly sectioned biopsy specimens under a microscope to assign a Gleason grade group (GG). Unfortunately, the assigned GG is not always associated with a patient’s outcome in part because of the limited sampling of spatially heterogeneous tumors achieved by 2-dimensional histopathology. In this study, open-top light-sheet microscopy was used to obtain 3-dimensional pathology data sets that were assessed by 4 human readers. Intrabiopsy variability was assessed by asking readers to perform Gleason grading of 5 different levels per biopsy for a total of 20 core needle biopsies (ie, 100 total images). Intrabiopsy variability (Cohen k) was calculated as the worst pairwise agreement in GG between individual levels within each biopsy and found to be 0.34, 0.34, 0.38, and 0.43 for the 4 pathologists. These preliminary results reveal that even within a 1-mm-diameter needle core, GG based on 2-dimensional images can vary dramatically depending on the location within a biopsy being analyzed. We believe that morphologic assessment of whole biopsies in 3 dimension has the potential to enable more reliable and consistent tumor grading. 

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4. Altered physical phenotypes of leukemia cells that survive chemotherapy treatment

   https://doi.org/10.1093/intbio/zyad006  

   Ly, Chau ; Ogana, Heather ; Kim, Hye Na ; Hurwitz, Samantha ; Deeds, Eric J. ; Kim, Yong-Mi ; Rowat, Amy C. ( May 2023 , Integrative Biology)

   The recurrence of cancer following chemotherapy treatment is a major cause of death across solid and hematologic cancers. In B-cell acute lymphoblastic leukemia (B-ALL), relapse after initial chemotherapy treatment leads to poor patient outcomes. Here we test the hypothesis that chemotherapy-treated versus control B-ALL cells can be characterized based on cellular physical phenotypes. To quantify physical phenotypes of chemotherapy-treated leukemia cells, we use cells derived from B-ALL patients that are treated for 7 days with a standard multidrug chemotherapy regimen of vincristine, dexamethasone, and L-asparaginase (VDL). We conduct physical phenotyping of VDL-treated versus control cells by tracking the sequential deformations of single cells as they flow through a series of micron-scale constrictions in a microfluidic device; we call this method Quantitative Cyclical Deformability Cytometry. Using automated image analysis, we extract time-dependent features of deforming cells including cell size and transit time (TT) with single-cell resolution. Our findings show that VDL-treated B-ALL cells have faster TTs and transit velocity than control cells, indicating that VDL-treated cells are more deformable. We then test how effectively physical phenotypes can predict the presence of VDL-treated cells in mixed populations of VDL-treated and control cells using machine learning approaches. We find that TT measurements across a series of sequential constrictions can enhance the classification accuracy of VDL-treated cells in mixed populations using a variety of classifiers. Our findings suggest the predictive power of cell physical phenotyping as a complementary prognostic tool to detect the presence of cells that survive chemotherapy treatment. Ultimately such complementary physical phenotyping approaches could guide treatment strategies and therapeutic interventions.

   Insight box Cancer cells that survive chemotherapy treatment are major contributors to patient relapse, but the ability to predict recurrence remains a challenge. Here we investigate the physical properties of leukemia cells that survive treatment with chemotherapy drugs by deforming individual cells through a series of micron-scale constrictions in a microfluidic channel. Our findings reveal that leukemia cells that survive chemotherapy treatment are more deformable than control cells. We further show that machine learning algorithms applied to physical phenotyping data can predict the presence of cells that survive chemotherapy treatment in a mixed population. Such an integrated approach using physical phenotyping and machine learning could be valuable to guide patient treatments.

    

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5. Classifying Non-Dementia and Alzheimer’s Disease/Vascular Dementia Patients Using Kinematic, Time-Based, and Visuospatial Parameters: The Digital Clock Drawing Test

   https://doi.org/10.3233/JAD-201129  

   Davoudi, Anis ; Dion, Catherine ; Amini, Shawna ; Tighe, Patrick J. ; Price, Catherine C. ; Libon, David J. ; Rashidi, Parisa ( June 2021 , Journal of Alzheimer's Disease)

   Bondi, Mark (Ed.)

   Background: Advantages of digital clock drawing metrics for dementia subtype classification needs examination. Objective: To assess how well kinematic, time-based, and visuospatial features extracted from the digital Clock Drawing Test (dCDT) can classify a combined group of Alzheimer’s disease/Vascular Dementia patients versus healthy controls (HC), and classify dementia patients with Alzheimer’s disease (AD) versus vascular dementia (VaD). Methods: Healthy, community-dwelling control participants (n = 175), patients diagnosed clinically with Alzheimer’s disease (n = 29), and vascular dementia (n = 27) completed the dCDT to command and copy clock drawing conditions. Thirty-seven dCDT command and 37 copy dCDT features were extracted and used with Random Forest classification models. Results: When HC participants were compared to participants with dementia, optimal area under the curve was achieved using models that combined both command and copy dCDT features (AUC = 91.52%). Similarly, when AD versus VaD participants were compared, optimal area under the curve was, achieved with models that combined both command and copy features (AUC = 76.94%). Subsequent follow-up analyses of a corpus of 10 variables of interest determined using a Gini Index found that groups could be dissociated based on kinematic, time-based, and visuospatial features. Conclusion: The dCDT is able to operationally define graphomotor output that cannot be measured using traditional paper and pencil test administration in older health controls and participants with dementia. These data suggest that kinematic, time-based, and visuospatial behavior obtained using the dCDT may provide additional neurocognitive biomarkers that may be able to identify and tract dementia syndromes. 

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