Zarah Alyashi Reoprt 2 Optical Biopsy using Spectroscopy Techniques and Artificial Intelligence for Cancer Diagnosis In this lecture, the speaker mentioned two types of cancerous cell diagnosis first one by using biopsy with histopathological analysis. this method is the gold standard and it is oldest method for detect cancer cell but this technique has many disadvantages that making this way not effective in some advance levels of cancer because this type takes long time and weeks, this leads to loss of the time. Also, it is very invasive and subjective. then they developed technique using light and this is effective method for diagnose cancer called optical biopsy. in this method, they shine the light onto the sample and based on the signal that collect from the sample and they can define the signal between different other signals based on those matter interaction and different time. this method has features lie in taking short time it is rapid. furthermore, it can avoid subjectivity and non-invasive. Also, giving diagnostic information for early detection the cancerous tumors. Optical biopsy method includes many different techniques such as fluorescence spectroscopy, Raman spectroscopy, SRS, CARS...etc. First, Resonance Raman Spectroscopy has two types: one that using (RRS) Resonance Raman spectroscopy, they couldn't detect some fingerprints of the molecules. So, using (NRRS) Non-Resonance Raman spectroscopy, they can analyze the tissue at the molecular level, also penetrate deep and measure large volume relatively. next, the speaker explained types of analytical method that depends on the data. For example, PCA uses algebra principle to find the components (PCs) of a data set and this is most strongly but these values could be negative. Then, the speaker listed other types analytical data like Non-negative Matrix Factorization (NMF) . Also, some of classifications such as Support Vector Machine (SVM) which dependent on data points that lie most closely to the decision boundary. Finally, he talked about three projects, the first project is only brain cancer. Then the other projects the breast cancer, it divided into common types like (IDC) Invasive Ductal carcinoma • Of the 245,000 American women diagnosed with invasive breast cancer each year, about 75% of them have invasive ductal carcinoma; e.g. MCF-7 cell line • Invasive lobular carcinoma (ILC) • ILC is the second most common form of invasive breast cancer, accounting for 10 to 15% of breast cancer cases. • Triple Negative Breast Cancer (BRCA) • About 10 to 15% of breast cancers are classified as triple negative (TNBC) because they lack receptors for estrogen, progesterone, and HER2. • Harder to treat because they don't respond to drugs that target estrogen, progesterone, or HER2 receptors. • eg. MDA-MB-231 • Ductal carcinoma in situ • The most common form of noninvasive breast cancer (Stage 0). Less common types such as Sarcomas, Angiosarcomas ,Phyllodes and Paget's diseas ...

1. Zarah Alyashi
Reoprt 2
Optical Biopsy using Spectroscopy Techniques and Artificial
Intelligence for Cancer Diagnosis
In this lecture, the speaker mentioned two types of cancerous
cell diagnosis first one by using biopsy with histopathological
analysis. this method is the gold standard and it is oldest
method for detect cancer cell but this technique has many
disadvantages that making this way not effective in some
advance levels of cancer because this type takes long time and
weeks, this leads to loss of the time. Also, it is very invasive
and subjective. then they developed technique using light and
this is effective method for diagnose cancer called optical
biopsy. in this method, they shine the light onto the sample and
based on the signal that collect from the sample and they can
define the signal between different other signals based on those
matter interaction and different time. this method has features
lie in taking short time it is rapid. furthermore, it can avoid
subjectivity and non-invasive. Also, giving diagnostic
information for early detection the cancerous tumors.
Optical biopsy method includes many different techniques such
as fluorescence spectroscopy, Raman spectroscopy, SRS,
CARS...etc. First, Resonance Raman Spectroscopy has two
types: one that using (RRS) Resonance Raman spectroscopy,

2. they couldn't detect some fingerprints of the molecules. So,
using (NRRS) Non-Resonance Raman spectroscopy, they can
analyze the tissue at the molecular level, also penetrate deep
and measure large volume relatively. next, the speaker
explained types of analytical method that depends on the data.
For example, PCA uses algebra principle to find the components
(PCs) of a data set and this is most strongly but these values
could be negative. Then, the speaker listed other types
analytical data like Non-negative Matrix Factorization (NMF) .
Also, some of classifications such as Support Vector Machine
(SVM) which dependent on data points that lie most closely to
the decision boundary.
Finally, he talked about three projects, the first project is only
brain cancer. Then the other projects the breast cancer, it
divided into common types like (IDC) Invasive Ductal
carcinoma • Of the 245,000 American women diagnosed with
invasive breast cancer each year, about 75% of them have
invasive ductal carcinoma; e.g. MCF-7 cell line • Invasive
lobular carcinoma (ILC) • ILC is the second most common form
of invasive breast cancer, accounting for 10 to 15% of breast
cancer cases. • Triple Negative Breast Cancer (BRCA) • About
10 to 15% of breast cancers are classified as triple negative
(TNBC) because they lack receptors for estrogen, progesterone,
and HER2. • Harder to treat because they don't respond to drugs
that target estrogen, progesterone, or HER2 receptors. • eg.
MDA-MB-231 • Ductal carcinoma in situ • The most common
form of noninvasive breast cancer (Stage 0). Less common types
such as Sarcomas, Angiosarcomas ,Phyllodes and Paget's
disease (DCIS that involves the nipple, causing it to look red
and scaly.) The third Project is differentiating prostate cancer
cell lines with different metastatic abilities using native
fluorescence spectroscopy and machine learning methods