Cancer is the leading cause of death at the global level. The global war against cancer is not a new one. It has been going on for decades now. The global objective to fight and win over cancer is so strong that everyone, from researchers to scientists, is collaborating tirelessly to end this worldwide burden.
Introduction
The field of computer science has shown remarkable and promising results in the past in this battle against cancer. Growing expenditure directed towards the research and development proficiencies involving the application of computer science in cancer diagnosis and treatment is a positive sign for the global healthcare industry. But before understanding the role of computer science in oncology, let us look at the recent global cancer statistics.
Fig.1: Cancer Statistics 2023 (U.S.)
Source: Cancer.org
The most typical cancer diagnoses for men and women in 2023 are shown in Figure 1. Nearly half (48%) of all incident instances of cancer in males are for the prostate, lung, and bronchus (hereafter lung), and colorectal cancers (CRCs), with 29% of diagnoses coming from prostate cancer alone. Breast cancer alone accounts for 31% of all cancer diagnoses in women, whereas lung cancer, CRC, and breast cancer together account for 52% of all new diagnoses. The projected number of new cases and fatalities from the top ten cancer types in the United States by sex in 2023. Estimates are rounded to the closest 10, and cases don't include in situ cancers of the urinary bladder or basal and squamous cell skin cancers.
Fig.2: Trends in Cancer Incidence (1975–2019) and Mortality (1975–2020) Rates by Sex (U.S.)
Source: Cancer.org
Figure 2 depicts long-term trends in overall cancer incidence rates, which reflect patterns in cancer-risk behaviors and changes in medical practice, such as cancer screening tests. For isntance, the early 1990s spike in male incidence reflects a surge in the detection of asymptomatic prostate cancer as a result of widespread rapid uptake of prostate-specific antigen (PSA) testing among previously unscreened men. After that, men cancer incidence decreased until around 2013, then stabilized until 2019. Women's rates were relatively stable until the mid-1980s when they began to rise slowly by 0.5% per year.
As a result, the gender gap is gradually closing, with the male-to-female incidence rate ratio falling from 1.59 (95% CI, 1.57-1.61) in 1992 to 1.14 (95% CI, 1.14-1.15) in 2019. However, risk differences vary greatly by age. For instance, females have about 80% higher rates than males between the ages of 20 and 49, while men have nearly 50% higher rates between the ages of 75 and older.
C & CSc: Cancer and Computer Science
These figures not only highlight the dreadful reality of this prevalent illness but are also crucial for academics, policymakers, and other professionals since they must first comprehend the effects cancer has on the global population before coming up with measures to fight it.
A startling call to action to an unlikely pool of candidates—computer scientists—is among those recently offered techniques. These recent advancements in the battle against cancer have the potential to fundamentally alter the research landscape for that field and, ultimately, save thousands of lives. This is but one potential method by which computer science could harvest big data to progress the sciences as a whole seriously.
Siddhartha Mukherjee, an Indian-born American physician and scientist, writes in his book The Emperor of All Maladies: A Biography of Cancer about the startlingly recent finding that cancer is a hereditary disease predominantly brought on by mutations in our DNA. So, due to these mutations, cancer tumors have an inconceivable diversity that makes them challenging to eradicate entirely.
As a result, it has been suggested that by sequencing the genome of a cancer tumor, which is essentially the process of translating or decoding the enigmatic language that makes up the tumor's unique DNA sequence, doctors will then be able to prescribe individualized, targeted treatment for each cancer patient with the aim of either stopping the cancer's growth or curing it completely.
Computer scientists such as David Patterson, one of the directors of the Algorithms, Machines, and People Laboratory (AMP Lab) at UC Berkeley, has been motivated by this in their work. The human eye cannot possibly do such a task by itself. To correctly and successfully absorb and analyze this huge volume of data at breakneck speed would require the engagement of some of the most potent cognitive computing platforms in the world, such as IBM's Watson. There will be three outcomes from computer scientists' involvement in this highly technological process:
Lowering information processing costs can help make tailored treatment accessible to everybody
It might lead to developing a cancer genome repository accessible to researchers and medical experts
It will be able to find a tiny needle in a very large haystack by using the aforementioned repository to find individualized, targeted therapy for each unique tumor among the countless possible drug combinations
Computational Oncology as an Extension to Computer Science in Oncology
Computational biology forges a connection between physical science and oncology. Computational oncology is a relatively new term in medicine that is starting to acquire traction. Some people might be surprised to learn that huge medical institutions all over the world are creating complete departments labeled as such. More and more time, effort, money, and resources are being devoted to learning how cancer spreads and can ultimately be permanently removed from the body.
With everything, the likelihood of developing long-lasting solutions increases with the information gathered. In order to organize tumor growth pathways, tumor biology, bioinformatics, and tumor marker profiles and construct predictive models for treatments based on all of this data, computational oncology organizes the molecular aspects of cancer.
Computer models are used in computational oncology to produce tumor marker analytics that are helpful in, precision medicine, population screening, and individual cancer cell modeling. This knowledge makes it more likely that specific medications or treatment techniques will offer long-term remedies for disease in a person with cancer.
For many years-and in certain circumstances, even today-the majority of people with cancer have received treatment that is only "broadly applied." When molecular markers are absent or less useful in determining the precise reasons why particular treatment approaches are effective for some patients but not others. In order to better serve patients, computational oncology departments can take the wealth of information about our genome that next-generation sequencing (NGS) has made available in both healthy and diseased cells and organize it into a database.
To manage all facets of this emerging field of medicine, some departments look for people with skill sets in either computer science or laboratory science. For educators, scientists, and clinicians, this field is expanding. By working together, we can increase our knowledge and skills in order to lessen the burden of cancer around the world, which is predicted to increase from 14.1 million new cases in 2012 to 23.6 million cases annually by 2030, according to the International Agency for Research on Cancer.
Data Bridge Market Research analyses that the cancer diagnostics market is expected to reach the value of USD 28.21 billion by the year 2029, at a CAGR of 7.29% during the forecast period. North America dominates the cancer diagnostics market due to the increasing presence of numerous biotechnology and medical device companies, increased funding available for research and development projects, and the region's high adoption of advanced technologies. Some of the major players operating in the cancer diagnostics market are Abbott. (U.S.), DiagnoCure Inc. (Canada), Thermo Fisher Scientific Inc. (U.S.), Illumina, Inc. (U.S.), QIAGEN (Germany), and F. Hoffmann-La Roche Ltd (Switzerland).
해당 연구에 대한 자세한 내용은 https://www.databridgemarketresearch.com/reports/global-cancer-diagnostics-market 에서 확인하세요.
''Microsoft의 10년 장기 야망''
마이크로소프트는 머신러닝과 알고리즘을 포함한 컴퓨터 과학을 활용하여 암과 싸우고 있습니다. 마이크로소프트 연구원들은 암을 정보 처리 시스템처럼 접근함으로써, 일반적으로 계산 과정을 모델링하는 데 사용되는 기법을 수정하여 생물학적 과정을 시뮬레이션할 수 있습니다.
이 회사의 궁극적인 목표는 암세포가 발견되는 즉시 신체에 암세포와 싸우도록 지시하는 분자 컴퓨터를 개발하는 것입니다. 마이크로소프트는 이를 데이터 기반 전략과 결합하여 머신러닝을 중심으로 질병 퇴치를 위한 노력을 기울이고 있습니다. 마이크로소프트는 분석 도구를 활용하여 기존의 생물학적 데이터를 수집하고 이를 활용하여 질병을 더 잘 이해하고 치료하고자 합니다.
이는 단순한 비유가 아닌 심오한 수학적 발견입니다. 생물학과 컴퓨팅은 극과 극으로 보일 수 있지만, 실제로는 가장 근본적인 차원에서 매우 깊은 연관성을 가지고 있습니다. 예를 들어, 머신러닝과 자연어 처리는 이용 가능한 연구 데이터를 분류하는 방법을 제공하는 데 활용되고 있으며, 이 데이터는 종양학자들에게 제공되어 환자에게 가장 효과적이고 맞춤화된 암 치료법을 개발할 수 있습니다.
현재 너무나 많은 정보가 존재하여 한 사람이 모든 정보를 읽고 이해하기는 어렵습니다. 머신러닝을 통해 인간보다 더 빠르고 간편하게 정보를 처리할 수 있습니다.
머신 러닝은 컴퓨터 비전과 결합되어 방사선 전문의가 환자의 종양이 어떻게 발달하고 있는지 더 잘 이해할 수 있도록 도와줍니다. 연구진은 앞으로 3D 스캔의 픽셀을 분석하여 이전 스캔 이후 종양이 얼마나 성장, 감소 또는 모양이 바뀌었는지 정확하게 판단하는 시스템을 개발하고 있습니다. 케임브리지 연구소 생물 계산 연구부장인 앤드류 필립스는 과학자들이 소프트웨어 산업의 선구자인 마이크로소프트의 유산에서 배울 수 있다고 말합니다. 그는 "컴퓨터 프로그래밍을 위해 발견한 기술을 사용하여 생물학을 프로그래밍할 수 있습니다."라고 덧붙였습니다. "이를 통해 훨씬 더 많은 용도와 더 나은 치료법이 개발될 것입니다."
필립스는 세포 내에 삽입하여 질병을 추적할 수 있는 분자 컴퓨터를 개발하고 있습니다. 센서가 질병이 암과 유사한 것으로 감지되면 질병 퇴치 반응이 시작됩니다. 이러한 유형의 연구는 기존 컴퓨팅을 활용하여 생명공학이나 의료 분야에 재활용할 수 있으며, 이를 통해 우리가 컴퓨터에 프로그래밍하는 것과 같은 방식으로 신체가 질병과 싸우도록 훈련될 수 있습니다.
필립스는 텔레그래프와의 인터뷰에서 연구가 아직 초기 단계에 있지만 "5~10년 안에" 이런 방식으로 질병을 퇴치할 수 있는 스마트 분자 시스템을 이식하는 것이 기술적으로 가능할 것이라고 말했습니다.
결론
암 연구는 점점 더 온라인으로 진행되고 있습니다. 컴퓨터 과학자들은 향후 10년 안에 암과 싸울 최고의 인재를 확보할 수 있기 때문에 적극적으로 참여해야 합니다. 암 종양의 유전체 시퀀싱을 통해 의료 전문가들은 머지않아 암의 확산을 늦추거나 멈추는 맞춤형 표적 치료법을 제공할 수 있을 것으로 기대됩니다.
컴퓨터 과학이 환자의 삶에 얼마나 빠르게 영향을 미치고 암 연구에 얼마나 빠르게 접목되었는지를 고려하면, 앞으로 몇 년은 컴퓨터 과학 못지않게, 어쩌면 그보다 더 생산적일 것이라고 예측하는 것이 타당해 보입니다. 향후 10년 동안 의료진은 건강한 조직과 병든 조직이 어떻게 발달하고 진화하는지에 대한 상세한 지도를 만들 수 있을 것으로 예상됩니다. 이러한 지도는 의료진이 새로운 암 진단 및 치료법을 개발하는 데 도움이 될 것입니다.
데이터 브리지 마켓 리서치(Data Bridge Market Research)에 따르면, 2022년 96억 4천만 달러 규모였던 의료 인공지능 시장은 2030년까지 2,729억 1천만 달러에 달할 것으로 예상되며, 2023년부터 2030년까지 연평균 성장률(CAGR)은 51.87%에 달할 것으로 전망됩니다. 의료 인공지능 시장은 제공 분야, 기술, 최종 사용자, 그리고 애플리케이션에 따라 세분화됩니다. 아시아 태평양 지역은 정부 차원의 인식 제고 정책 강화, 의료 관광 증가, 그리고 양질의 의료 서비스에 대한 수요 증가로 인해 2023년부터 2030년까지 가장 높은 성장률을 기록할 것으로 예상됩니다.
해당 연구에 대한 자세한 내용은 https://www.databridgemarketresearch.com/reports/global-artificial-intelligence-in-healthcare-market 에서 확인하세요.
