7.1. disease#

  • ECT - shock therapy given to brain when patient is struggling with severe mental illness (~100k patients in the US / year)

7.1.1. 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

7.1.2. 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

7.1.3. pathology#

7.1.3.1. 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

7.1.3.2. 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

7.1.3.3. datasets#

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

7.1.4. cancer#

7.1.4.1. 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

7.1.4.1.1. 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

7.1.4.2. prostate cancer#

7.1.4.3. 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)