To do:
-brief infroduction on IHC staining
<under_progress: raunak>

Histopathology

Important points in histopathology

Diagnostic and Understanding Challenges and opportunities

  1. Identifying tumors in lymph nodes in Hematoxylin-eosin (H&E) stained histopathological images is generally laborious and error-prone, esp. for small tumor foci. Immunohistochemical (IHC) staining can improve sensitivity but increases workload, costs and delays reporting. AI can achieve comparable performance to pathologists in H&E stained images and help them improve morphologic detection of metastatic tumors in lymph nodes  \cite{Liu2019}.
  2. Mitosis count for prognosis: write about it
  3. Predicting invasive vs. non-invasive (tumor vs. not tumor) is relatively less challenging compared to distinguishing the sub-types in non-invasive ones: benign, atypia, Ductal Carcinoma In Situ (DCIS) where the diagnostic disagreements are remarkably high among pathologist \cite{Mercan2019}. The authors therefore propose a multi-stage approach (semantic segmentation into 8 tissue labels followed by feature extraction and diagnostic classification) for classifying the breast lesions into four classes: benign, atypia, DCIS, and invasive breast cancer. They design hierarchical scheme with a single diagnosis at a time as follows: (1) invasive vs. noninvasive diagnosis, (2) if noninvasive, classify into benign vs pre-invasive lesions, and (3) if pre-invasive, classify into DCIS or atypia. The main limitation here is that the Regions of Interests (ROI) have to be manually selected from Whole Slide Images (WSIs) before feeding each ROI into the multi-stage classifier pipeline.
  4. In \cite{Gecer2018}, the authors provide a method to first automatically identify ROI, then classify into five diagnostic categories of ductal proliferations: non-proliferative changes, proliferative changes, atypical ductal hyperplasia (ADH), ductal carcinoma in situ(DCIS), and invasive ductal carcinoma (IDC). Question: Are non-proliferative vs proliferative eqv. to benign vs. malignant or invasive vs. non-invasive? The authors record the zoom activity of pathologists to create ground truth data for ROIs.
  5. In \cite{Couture2018}, the authors propose automatically analysing H&E stained breast tumor microarray (TMA) images to identify patients that would benefit from molecular testing by predicting tumor grade, histologic subtype, estrogen receptor (ER) status, PAM50 intrinsic breast cancer subtype, and risk of recurrence score (ROR-PT). Although RNA-based, multi-gene molecular assays are available and widely used for patients with ER-positive/HER2-negative breast cancers, these tests are costly and unavailable in many countries. Thus, this kind of methods for inferring molecular subtype just from H&E stained images may reduce the number of patients sent for further testing suspecting benefit from further genomic tests, saving cost and time.

AI challenges and Potential Solutions

H&E slides variation

Tissue preparation, staining protocols and oxidation in the lab makes the whole slide images different when taken from different centers. Digitization by a camera introduces further variation. Transforming the color space and trying to get the color statistics of individual images towards a reference statistics can be helpful as suggested in page 861 of \cite{Liu2019}.  

Grading

HE slides on lymph nodes biopsy

1. macrometastasis 2. micrometastatsis 3. isolated tumor cells (ITC) 4. negative  Also useful to localize in the images micrometastasis or ITC cases.
  

Glossary

Lymph node: Also known as lymph gland, are ovoid or kidney-shaped organ of the lymphatic system which comprises of a lymphatic vessels carrying a clear fluid lymph. Lymph nodes are widely present throughout the body and are linked by lymphatic vessels. Lymph nodes condition play an important role in staging cancer which consequently decides the treatment plan and prognosis.
FNA: One of the three common biopsy techniques: fine-needle aspiration (FNA), vacuum-assisted biopsy and surgical biopsy.