The Surprising Way Data Science Helps In Cancer Research.

Cancer is one of the fastest evolving, ever changing and an adapting complex disease. In order to understand this complexity, there lies a need to study the snapshots of the tumor’s genetic make up. However, these snapshots need to be acquired frequently to understand the process behind the evolution of the tumor. Humongous amounts of data is generated as a result of the measurements underlying these snapshots. The important patterns identified in the specific cancers can be leveraged to develop models that diagnose and treat the disease. The complexity of the disease can be solved by data science, involving sequencing methods. This can be done with intelligent machines using the genomic data sets and artificial intelligence as a tool. 

 

WHY CANCER IS ONE OF THE DEADLIEST DISEASE AND STILL MISSING A PERMANENT CURE?

Cancer refers to any one of a large number of diseases characterized by the development of abnormal cells that divide uncontrollably and have the ability to infiltrate and destroy normal body tissue. Cancer often has the ability to spread throughout your body.Cancer is the second-leading cause of death in the world. 

Part of the reason for having no cancer "cure" is semantics. There will never be a single cancer cure because cancer refers to a family of more than 100 different diseases characterised by abnormal cell growth. But, with the help of modern technologies and harnessing the power of data science, we can predict cancer in very early stages and thereby can save lives .

CANCER TODAY WILL NOT BE SAME AS CANCER TOMORROW

HIGHLIGHTS 

>> Data Science + Deep learning + Machine learning = Precise cancer imagining +  

      Rapid cancer diagnosis + Saving lives of millions.

>> How data science helps in early cancer diagnosis ?

>> How data science can improve treatments for cancer and help doctors fight cancer?

Artificial Intelligence and Data Science: The future of oncology 

In the machine age, researchers have started scratching the surface of all the ways to use AI & DS in oncology.

And with each passing day, they are coming up with new applications of AI & DS in cancer research and care.

Clinical decision-making

Decision-making is the goal of leveraging data science in clinical cancer research and care. Hence, to discover the best treatments for patients, clinicians must make data-driven decisions. However, analyzing a large amount of clinical data collected from different sources like biological data, electronic medical records (EMR), molecular/non-molecular imaging, etc. brings new opportunities.

The ability to extract rich insights from vast depots of clinical data and predict outcomes using data science and ML models provide huge potential to clinicians. Now, let us look at some crucial clinical decision scenarios where AI can become useful.

• Disease detection

• Lesion segmentation

• Treatment selection

• Response assessment

• Clinical prediction

• Cancer imaging and diagnosis

Nowadays, AI-powered tools and techniques are widely utilized to turn medical images into biomarkers and make cancer diagnostic tests much more efficient. AI can streamline different types of imaging processes within oncology like Positron Emission Tomography (PET), radiation risk, clinical photographs, digital mammography, etc.

For instance, now radiologists utilize AI and train ML algorithms to identify different patterns of myeloma infiltration in full-body MRI images. In fact, the scientists of the US-based National Cancer Institute (NCI) under its intramural research program are already leveraging AI for the automated detection of precancerous lesions using cervical images.

Cancer prognosis

The future of cancer prognosis is AI-enabled and data-driven. AI can assist radiologists and pathologists in early cancer prognosis. ML and Deep Learning algorithms also help them to predict three significant aspects of cancers – susceptibility, recurrence, and survival.

Predictive analytics can prove to be useful in clinical cancer prognosis/prediction in the following ways:

>>Clinical oncology: Predict outcomes of the drugs; estimate the risks of using it and future cancer methodologies.

Pathology: Get rich insights on biopsy reads; predict the optimal treatment for a cancer patient and perform accurate prognosis.

>>Radiomics: Predict the aggressiveness of disease and the response of particular cancer treatment or surgery.

>>Precision medicine

To fight cancer, all that a patient requires is the right drug at the right time i.e. precision medicine. The key objective of precision oncology is to translate patient data to individualized therapies as quickly as possible. And AI opens a new door for genomic sequencing that can help oncologists to process genetic mutations for precision medicine more precisely. The most used ML models in precision medicine include:

• Deep learning

• Logistic regression

• Linear regression

• Decision tree

Hence, we can say that the next-generation AI, ML and NLP (Natural Language Processing) algorithms are underpinning a whole new revolution in precision medicine, making breakthroughs to usher in a new era of healthcare intelligence.

Drug discovery and development

The process of developing drugs, in particular, cancer drugs, costs millions of dollars and extends up to ten or fifteen years. So, biotech and healthcare companies have started turning towards the adoption of AI for rapid and smarter clinical trials. AI-enabled clinical trials empower the pharmaceutical industry to discover novel cancer drugs and bring them to the market faster.

Combining AI, ML and genomic data, clinicians can not only discover drugs faster but also repurpose and develop effective cancer drugs in a  matter of days. Let us see some uses of ML and data science in biomarker discovery.

1. Automate the drug discovery process

2. Repurpose existing drugs for new indications

3. Predict how compounds work inside a specific cancer

4. Lower the timeline and risk of oncology drug development

5. Personalised cancer treatment.

Until now, the standard of care to fight cancer was to follow the same ‘cut-burned poison’ approach for every individual.

But the type of cancer of every patient will not be the same, so every individual cannot be treated the same.

This means, every individual requires personalised cancer care and treatment, and AI makes it a lot easier.

For instance, AI is changing the way lung cancer patients receive radiation therapy today.

It combines ML and EHR data to generate a data-driven, personalized dose of radiation

for each patient during the cancer treatment.

So, we can say that artificial intelligence in cancer treatment can assist doctors in

tailoring treatment plans separately for each cancer patient.

Data Science In Early Cancer Treatment

Data Science can be integral to the early diagnosis and subsequent treatment of cancer

in many patients.

This is because knowing a patient’s symptoms and prognosis, and then comparing it to

a database of people with the same symptoms can help medical teams decide how they

want to treat cancer and begin the treatment process.

Big Data helps analyse this massive amount of information available and also helps to categorise data according to age,

race and

gender so that more detailed information is available, forming patterns which doctors can

use to treat cancer.

Big Data is also able to predict long term solutions based on the availability of previous

cases, both successful or not, and thus help doctors determine the best course for actions.

Curing Cancer

Big Data cannot cure cancer on its own. However, scientists can make use of it, along

with other intelligent machines, to study the complex ways in which cancer cells multiply

and form tumours. Before Big Data, it took scientists and researchers decades to realize

the link between lung cancer and cigarettes. Now, with the help of modern technology,

research facilities only need a hospital’s approval to check its records with histories of

cancer cases. Instead of putting a lot of manpower and work into looking for patterns,

these scientists can easily rely on Big Data and AI to help analyse and understand patterns.

Many government organizations dedicated to finding the cure for cancer have realized

that it is going to be near possible to find a cure for cancer without using AI and Big Data. Because of this, there is a lot of development happening and organizations such as Million Veteran Program in the United States and the Cancer Genome Atlas in the UK are working towards using Big Data to help create human genomes, open to researchers for analysis

via the cloud.

The point is to study as many cases as possible in order to get newer insights as quickly as possible. The sooner we understand how all types of cancers are formed, the sooner we will get to actually curing them all, once and for all.