The World Health Organisation (WHO) has a very grim view on cancer and its widespread presence in India. With a million new cases being reported every year, cancer seems to be tightening its grip on the country. Some experts are predicting that occurrence of the killer disease is expected to rise five-fold by 2025. Statistics show that cancer cases in India have increased from 700 to about 1000 per million population.
Today, apart from timely detection, the biggest challenge for oncologists is that of standardisation (of treatment). Taking breast cancer as an example, data shows that no two cancers are the same - pre-menopausal differs from post-menopausal breast cancer and hence the treatment for each is different. Yet most patients are offered standardised treatments that rarely look into issues of family history, individual case history, and other issues such as pathology along with the stage of life in which it has occurred. Consequently, personalised treatments are relatively unknown, with most practitioners using a standard approach with minor tweaks or variations.
Big data analytics - an emerging game-changer in cancer treatment
Data discovery and Business Intelligence (BI) tools are proving to be game-changers in cancer detection and management. Researchers are predicting that Big Data analysis has the potential to accelerate cancer treatment and to make good on the promise of more personalised medicine and treatment.
Data is being used to assist oncologists in providing tailor-made treatments on the basis of patient history, biopsy specimens and other related data. Institutions across the world are now busy collating myriad forms of cancer-related data from patient case histories as well as from global research and surveys. Huge quantities of this data is now being analysed and examined to spot findings that can be used to create new and non-conventional therapies that can lead to more personalised treatments. For example, researchers in the United States are now sifting through hundreds of gigabytes of image data from thousands of patients to try to find differences that distinguish the different subtypes of breast cancers from each other. Given that no cancer is the same, this could be the holy grail of cancer treatment. For patients who have non-recurrent cancer, this could lead to milder treatment and therapies. The opposite might hold true for those with more aggressive or recurrent strains of the disease.
Not just content with this, researchers are using risk stratification modules to crawl through millions of health records in a population to understand patterns, trends and patient similarity metrics leveraging natural language processing systems. This also takes into context unstructured data like physician notes and discharge documents that are not often taken into account for analysis, reinforcing the importance of Big Data analytics in this area of treatment. Big Data would also help researchers and practitioners with specific details of how thousands of medicines interact with the human body, making suggestions of those it thinks might interact beneficially with cancer-infested cells. Interestingly it was recently announced that 14 cancer institutes across the United States and Canada would be using analytics and artificial intelligence (AI) to match cancer patients with the treatments most likely to help them. In addition to recommending relevant drugs most likely to treat a particular cancer, AI could also suggest therapies not tried before.
The India story
Of the 1.8 million people living with cancer in 2012, about 683,000 (or a little less than half) did not survive the disease. According to the Global Burden of Cancer study, India has higher rates of mouth cancer globally. Specifically for men, prostate cancer is the biggest threat while among women, its breast cancer. However. it's stomach cancer that is the leading cause of death for men and women in India (while globally lung cancer is the biggest killer).
The push for Big Data Analytics in cancer management and treatment is now being seen in India. For example, in Kerala, the state capital Thiruvananthapuram regularly publishes information on magnitude, pattern, diagnostic details, stage of diagnosis and treatment modalities of patients reporting to the Regional Cancer Centre (RCC). The data in turn is used for further epidemiological research on cancer, patient care etc. In addition to this, the city also has the Hospital Based Cancer Registry (HBCR), which started in 1982 under the network of National Cancer Registry Programme (NCRP) of Indian Council of Medical Research (ICMR).
In Bangalore, The National Cancer Registry Programme (NCRP) was initiated by the Indian Council of Medical Research (ICMR) using a network of cancer registries across the country. The main objectives of the NCRP is to generate reliable data on the magnitude and patterns of cancer in the country and undertake epidemiological studies based on results of registry data.
Using this, it is helping design, plan, monitor and evaluate cancer control activities under the National Cancer Control Programme (NCCP).
In the future, a test to help Indians make therapy decisions, such as receiving chemotherapy for aggressive tumours or forgoing it if the cancer is not predicted to progress, could be possible using the analysis of this data. This can perhaps even be priced in a way that makes it accessible to all. Equally important is that turnaround times for cancer detection and diagnosis can potentially be shortened from a few weeks to almost two-three days, saving enormous time and money.
It is essential that hospitals and clinics adopt electronic health records (EHR) and online hospital information systems (HIS) that can further integrate with the NCRP. The collection and management of this data needs to be systematic and for that proper records need to be maintained. This analysis of data along with demographical statistics needs to be correlated with existing data sets for more effective outcomes around cancer management and treatment.
Conclusion
Today, the use of Big Data to help build medical assays to predict cancer recurrence, progression and a response to therapy is no longer a flight of fancy. The fight against cancer is the search for the holy grail of medicine. Unfortunately, almost everyone will be affected at some point in their lives, either personally or through a loved one. The fight continues but leveraging Big Data analytics and machine intelligence can help speed up the process of finding the elusive cure, thereby ensuring that there is a definitive light at the end of the tunnel.
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Today, apart from timely detection, the biggest challenge for oncologists is that of standardisation (of treatment). Taking breast cancer as an example, data shows that no two cancers are the same - pre-menopausal differs from post-menopausal breast cancer and hence the treatment for each is different. Yet most patients are offered standardised treatments that rarely look into issues of family history, individual case history, and other issues such as pathology along with the stage of life in which it has occurred. Consequently, personalised treatments are relatively unknown, with most practitioners using a standard approach with minor tweaks or variations.
Data is being used to assist oncologists in providing tailor-made treatments on the basis of patient history, biopsy specimens and other related data.
Big data analytics - an emerging game-changer in cancer treatment
Data discovery and Business Intelligence (BI) tools are proving to be game-changers in cancer detection and management. Researchers are predicting that Big Data analysis has the potential to accelerate cancer treatment and to make good on the promise of more personalised medicine and treatment.
Data is being used to assist oncologists in providing tailor-made treatments on the basis of patient history, biopsy specimens and other related data. Institutions across the world are now busy collating myriad forms of cancer-related data from patient case histories as well as from global research and surveys. Huge quantities of this data is now being analysed and examined to spot findings that can be used to create new and non-conventional therapies that can lead to more personalised treatments. For example, researchers in the United States are now sifting through hundreds of gigabytes of image data from thousands of patients to try to find differences that distinguish the different subtypes of breast cancers from each other. Given that no cancer is the same, this could be the holy grail of cancer treatment. For patients who have non-recurrent cancer, this could lead to milder treatment and therapies. The opposite might hold true for those with more aggressive or recurrent strains of the disease.
In the future, a test to help Indians make therapy decisions, such as receiving chemotherapy for aggressive tumours or forgoing it if the cancer is not predicted to progress, could be possible...
Not just content with this, researchers are using risk stratification modules to crawl through millions of health records in a population to understand patterns, trends and patient similarity metrics leveraging natural language processing systems. This also takes into context unstructured data like physician notes and discharge documents that are not often taken into account for analysis, reinforcing the importance of Big Data analytics in this area of treatment. Big Data would also help researchers and practitioners with specific details of how thousands of medicines interact with the human body, making suggestions of those it thinks might interact beneficially with cancer-infested cells. Interestingly it was recently announced that 14 cancer institutes across the United States and Canada would be using analytics and artificial intelligence (AI) to match cancer patients with the treatments most likely to help them. In addition to recommending relevant drugs most likely to treat a particular cancer, AI could also suggest therapies not tried before.
The India story
Of the 1.8 million people living with cancer in 2012, about 683,000 (or a little less than half) did not survive the disease. According to the Global Burden of Cancer study, India has higher rates of mouth cancer globally. Specifically for men, prostate cancer is the biggest threat while among women, its breast cancer. However. it's stomach cancer that is the leading cause of death for men and women in India (while globally lung cancer is the biggest killer).
Turnaround times for cancer detection and diagnosis can potentially be shortened from a few weeks to almost two-three days, saving enormous time and money.
The push for Big Data Analytics in cancer management and treatment is now being seen in India. For example, in Kerala, the state capital Thiruvananthapuram regularly publishes information on magnitude, pattern, diagnostic details, stage of diagnosis and treatment modalities of patients reporting to the Regional Cancer Centre (RCC). The data in turn is used for further epidemiological research on cancer, patient care etc. In addition to this, the city also has the Hospital Based Cancer Registry (HBCR), which started in 1982 under the network of National Cancer Registry Programme (NCRP) of Indian Council of Medical Research (ICMR).
In Bangalore, The National Cancer Registry Programme (NCRP) was initiated by the Indian Council of Medical Research (ICMR) using a network of cancer registries across the country. The main objectives of the NCRP is to generate reliable data on the magnitude and patterns of cancer in the country and undertake epidemiological studies based on results of registry data.
Using this, it is helping design, plan, monitor and evaluate cancer control activities under the National Cancer Control Programme (NCCP).
In the future, a test to help Indians make therapy decisions, such as receiving chemotherapy for aggressive tumours or forgoing it if the cancer is not predicted to progress, could be possible using the analysis of this data. This can perhaps even be priced in a way that makes it accessible to all. Equally important is that turnaround times for cancer detection and diagnosis can potentially be shortened from a few weeks to almost two-three days, saving enormous time and money.
Leveraging Big Data analytics and machine intelligence can help speed up the process of finding the elusive cure...
It is essential that hospitals and clinics adopt electronic health records (EHR) and online hospital information systems (HIS) that can further integrate with the NCRP. The collection and management of this data needs to be systematic and for that proper records need to be maintained. This analysis of data along with demographical statistics needs to be correlated with existing data sets for more effective outcomes around cancer management and treatment.
Conclusion
Today, the use of Big Data to help build medical assays to predict cancer recurrence, progression and a response to therapy is no longer a flight of fancy. The fight against cancer is the search for the holy grail of medicine. Unfortunately, almost everyone will be affected at some point in their lives, either personally or through a loved one. The fight continues but leveraging Big Data analytics and machine intelligence can help speed up the process of finding the elusive cure, thereby ensuring that there is a definitive light at the end of the tunnel.
Image may be NSFW.
Clik here to view.
Like Us On Facebook | Image may be NSFW.
Clik here to view.
Follow Us On Twitter | Image may be NSFW.
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Contact HuffPost IndiaAlso see on HuffPost: