digital tracking

view markdown

alzheimer’s

  • overview
    • age-associated - tons of people get it
    • doesn’t kill you, secondary complications like pneumonia will kill you
    • rate is going up
    • very expensive to treat
  • declarative memories are affected by Alzheimer’s
    • these are memories that you know
  • first 2 areas to go in Alzheimer’s
    1. hippocampus
      • patient HM had no hippocampus
        • no anterograde memory - learning new things
      • hippocampus stores 1 day of info
        • offloading occurs during sleep (REM sleep) to prefrontal cortex, temporal lobe, V4
        • dreaming - might see images as you are offloading
    2. basal forebrain - spread synapses all over cortex
      • uses Ach
      • ignition key for entire cortex
  • alzheimer’s characteristics only found in autopsy
    • amyloid plaques
      • maybe A-beta causes it
      • A-beta comes from APP
      • A-beta42 binds to itself
        • prion (starts making more of itself)
        • this cycle could be exacerbated by injury
        • clumps and attracts immune system which kills local important cells
          • this could cause Alzheimer’s
        • rare genetic mutations in A-beta increase probability you get Alzheimer’s
        • anti-inflammation may be too late
        • can take drugs that increase Ach functions - ex. cholinergic agonists, cholinesterase inhibitors
    • tangles
      • tangles made of protein called Tau
    • most people think these are just dead cells resulting from Alzheimer’s but some think they cause it

parkinson’s

  • loss of substantia nigra pars compacta dopaminergic neurons
    • when you get down to 20% what you were born with
    • dopaminergic neurons form melanin = dark color
    • hits to head can give inflammation
  • know what they need to do - don’t have enough dopamine to act
  • treat with L Dopa -> something like dopamine -> take out globus pallidus
  • Lewy bodies are clumps of alpha synuclein - appear at dopaminergic synapses
    • clumps like A-beta42
    • associated with early-onset Parkinson’s (rare) associated with genetic mutations
  • bradykinesia - slowness of movement
  • age can give parksinson’s
  • no evidence that toxins can induce parkinsons
  • PTP/ pesticides can induce Parkinson’s in test animals
  • 1/500 people

pathology

basics

  • pathologists work with tissue samples either visually or chemically
    • anatomic pathology relies on the microscope whereas clinical pathology does not
  • pathologists convert from tissue image into written report
  • when case is challenging, may require a second opinion (v rare)
  • steps (process takes 9-12 hrs): tissue_prep
    • tissue is surgically removed
      • more tissue collected is generally better (gives more context)
      • this procedure is called a biopsy
      • much is written down at this step (e.g. race, gender, locations in organ, different tumors in an organ) that can’t be seen in slide alone
    • fixation: keeps the tissue stable (preserves dna also) - basicallly just soak in formalin
    • dissection: remove the relevant part of the tissue
    • tissue processor - removes water in tissue and substitute with wax (parafin) - hardens it and makes it easy to cut into thin strips
    • microtone - cuts very thin slices of the tissue (2-3 microns)
    • staining
      • H & E - hematoxylin and eosin stain - most popular (~80%) - colors the cells in a specific way, bc cells are usually pretty transparent
        • hematoxylin stains nucleic acids blue
        • eosin stains proteins / cytoplasm pink/red
      • immunohistochemistry (IHC) - tries to identify cell lineage: 10-15%
        • identifies targets
        • use antibodies tagged with chromophores to tag tissues
      • gram stain - highlights bacteria
      • giemsa - microorganisms
      • others…for muscle, fungi
    • viewing
      • usually analog - put slide on something that can move / rotate
      • whole-slide image (WSI) - resulting entire slide
        • tissue microarray (TMA) - smaller, fits many samples onto the same slide
      • with paige: put slide through digital scanner (only 5% or so of slides are currently digital)
    • later on, board meets to decide on treatment (based on pathology report)
      • usually some discussion betweeon original imaging (pre-biopsy) and pathologist’s interpretation
    • resection - after initial diagnosis, often entire tumor is removed (resection)
  • how can ai help?
    • can help identify small things in large images
    • can help with conflict resolution
  • after (successful) neoadjuvant chemotherapy, problem becomes more difficult
    • very few remaining cancer cells
    • cancer/non-cancer cells become harder to distinguish (esp. for prostate)
    • tumor bed is patchily filled with cancer cells - need to better clarify presence of cancer

papers

  • Deep Learning Models for Digital Pathology (BenTaieb & Hamarneh, 2019)
    • note: alternative to histopathology are more expensive / slower (e.g. molecular profiling)
    • to promote consistency and objective inter-observer agreement, most pathologists are trained to follow simple algorithmic decision rules that sufficiently stratify patients into reproducible groups based on tumor type and aggressiveness
    • magnification usually given in microns per pixel
    • WSI files are much larger than other digital images (e.g. for radiology)
    • DNNs can be used for many tasks: beyond just classification, there are subtasks (e.g. count histological primitives, like nuclei) and preprocessing tasks (e.g. stain normalization)
    • challenge: multi-magnification + high dimensions (i.e. millions of pixels)
      • people usually extract smaller patches and train on these
        • this loses larger context
        • one soln: pyramid representation: extract patches at different magnification levels
        • one soln: stacked CNN - train fully-conv net, then remove linear layer, freeze, and train another fully-conv net on the activations (so it now has larger receptive field)
        • one soln: use 2D LSTM to aggregate patch reprs.
      • challenge: annotations only at the entire-slide level, but must figure out how to train individual patches
        • e.g. use aggregation techniques on patches - extract patch-wise features then do smth simple, like random forest
        • e.g. treat as weak labels or do multiple-instance learning
          • could just give slide-level label to all patches then vote
      • can use transfer learning from related domains with more labels
    • challenge: class imbalance
      • can use boosting approach to increase the likelihood of sampling patches that were originally incorrectly classified by the model
    • challenge: need to integrate in other info, such as genomics
    • when predicting histological primitives, often predict pixel-wise probability maps, then look for local maxima
      • can also integrated domain-knowledge features
      • can also have 2 paths, one making bounding-box proposals and another predicting the probability of a class
      • alternatively, can formulate as a regression task, where pixelwise prediction tells distance to nearest centroid of object
      • could also just directly predict the count
    • can also predict survival analysis
  • Clinical-grade computational pathology using weakly supervised deep learning on whole slide images (campanella et al. 2019)
    • use slide-level diagnosis as “weak supervision” for all contained patches
    • 1st step: train patch-level CNNs using MIL
      • if label is 0, then all patches should be 0
      • if label is 1, then only pass gradients to the top-k predicted patches
    • 2nd step: use RNN (or another net) to combine info across S most suspicious tiles
  • Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes (diao et al. 21)
  • An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study (pantanowitz et al. 2020 - ibex)
    • 549 train, 2501 internal test slides, 1627 external validation
    • predict cancer prob., gleason score 7-10, gleason pattern 5, perneural invasion, cancer percentage
    • algorithm
      • GB classifies background / non-background / blurry using hand-extracted features for each tile
      • each tile gets predicted probability for 18 pre-defined classes (e.g. GP 3)
        • ensemble of 3 CNNs that operate at different magnifications
      • aggregation: 18-probability heatmaps are combined to calculate slide-level scores
        • ex (for predicting cancer): sum the cancer-related channels in the heatmap , apply 2x2 local averaging, then take max

datasets

  • ARCH - multiple instance captioning dataset to facilitate dense supervision of CP tasks

cancer

overview

  • tumor = neoplasm - a mass formation from an uncontrolled growth of cells
    • benign tumor - typically stays confined to the organ where it is present and does not cause functional damage
    • malignant tumor = cancer - comprises organ function and can spread to other organs (metastasis)
  • relation network based aggregator on patches
  • lymphatic system drains fluids (non-blood) from organs into lymph nodes
    • cancer often mestastasize through these
  • staging - describes where cancer is located and where it has spread
    • clinical staging - based on non-tissue things
    • pathological staging - elements of staging pTNM
      • size / depth of tumor “T”
      • number of lymph nodes / how many had cancer “N”
      • number of metastatic foci in non-lymph node organ “M”
      • these are combined to determine the cancer stage (0-4)
  • prognosis - chance of recovery

treatments

  • chemo
    • traditional chemotherapy disrupts cell replication
      • hair loss and gastrointestinal symptoms occur bc these cells also rapidly replicate
    • adjuvant chemotherapy - after cancer is removed, most common
    • neoadjuvant chemo - after biopsy, but before resection (when very hard to remove)
  • targeted therapies
    • ex. address genetic aberration found in cancer cells
    • immunotherapy - enhance body’s immune response to cancer cells (so body will attack these cells on its own)
      • want the antigens on the tumor to be as different as possible (so they will be characterized as foreign)
      • to measure this, can conduct total mutational burden (TMB) or miscrosatellite instability (MSI) test
        • genetic tests - hard to do by looking at glass slide
      • some tumors express receptors (e.g. CTLA4, PD1) that shut off immune cells - some drugs try to block these receptors

prostate cancer

bladder cancer

H & E slide

  • shape:
papillary flat can also have a combo
pap_blad flat_blad  
  • grade:
low high
low_grade_blad high_grade_blad
  • when shape is flat, grade often can’t be determined reliably
    • lots of names for uncertain (e.g. upump - uncertain malignant potential, or atypia)
  • much easier to decide shape than grade
  • once you find high grade, look for invasiveness (and deeper layers are worse)