Heart disease and cancer are the leading causes of death in the United States, and it’s increasingly understood that they share common risk factors, including tobacco use, diet, blood pressure, and obesity. Thus, a diagnostic tool that could screen for cardiovascular disease while a patient is already being screened for cancer, has the potential to expedite a diagnosis, accelerate treatment, and improve patient outcomes. 

In research published today in Nature Communications, a team of engineers from Rensselaer Polytechnic Institute and clinicians from Massachusetts General Hospital developed a deep learning algorithm that can help assess a patient’s risk of cardiovascular disease with the same low-dose computerized tomography (CT) scan used to screen for lung cancer. This approach paves the way for more efficient, more cost-effective, and lower radiation diagnoses, without requiring patients to undergo a second CT scan. 

“In this paper, we demonstrate very good performance of a deep learning algorithm in identifying patients with cardiovascular diseases and predicting their mortality risks, which shows promise in converting lung cancer screening low-dose CT into a dual screening tool,” said Pingkun Yan, an assistant professor of biomedical engineering and member of the Center for Biotechnology and Interdisciplinary Studies (CBIS) at Rensselaer.

Numerous hurdles had to be overcome in order to make this dual screening possible. Low-dose CT images tend to have lower image quality and higher noise, making the features within an image harder to see. Using a large dataset from the National Lung Screening Trial (NLST), Yan and his team used data from more than 30,000 low-dose CT images to develop, train, and validate a deep learning algorithm capable of filtering out unwanted artifacts and noise, and extracting features needed for diagnosis. Researchers validated the algorithm using an additional 2,085 NLST images.

The Rensselaer team also partnered with Massachusetts General Hospital, where researchers were able to test this deep learning approach against state-of-the-art scans and the expertise of the hospital’s radiologists. The Rensselaer-developed algorithm, Yan said, not only proved to be highly effective in analyzing the risk of cardiovascular disease in high-risk patients using low-dose CT scans, but it also proved to be equally effective as radiologists in analyzing those images. In addition, the algorithm closely mimicked the performance of dedicated cardiac CT scans when it was tested on an independent dataset collected from 335 patients at Massachusetts General Hospital.

“This innovative research is a prime example of the ways in which bioimaging and artificial intelligence can be combined to improve and deliver patient care with greater precision and safety,” said Deepak Vashishth, the director of CBIS.

Yan was joined in this work by Ge Wang, an endowed chair professor of biomedical engineering at Rensselaer and fellow member of CBIS. The Rensselaer team was joined by Dr. Mannudeep K. Kalra, an attending radiologist at Massachusetts General Hospital and professor of radiology with Harvard Medical School. This research was funded by the National Institutes of Health National Heart, Lung, and Blood Institute. 

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Using machine learning methods, researchers discovered the underlying genetic contributors of cancer.

Machine learning approaches could help detect mutational signatures in patients with cancer, revealing the genetic effects of the underlying contributors to the disease, a study published in eLife revealed.

The new technique uses machine learning algorithms to access and analyze what are called SuperSigs, or mutational signatures that reveal the genetic effects of the underlying contributors to cancer.

“Mutational signatures are important in current cancer research as they enable you to see the signs left by underlying factors, such as aging, smoking, alcohol use, UV exposure, and BRCA inherited mutations that contribute to the development of a cancer,” said study leader Cristian Tomasetti, PhD, associate professor of oncology at the Johns Hopkins Kimmel Cancer Center.

Dig Deeper

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The algorithm is classified as supervised because it is an analysis that includes known exposures during the training of the algorithm for the genetic analysis of a cancer. The most widely used mutational signatures used for assessing genomic data are classified as unsupervised because they do not take known exposures into consideration. Instead, it notes patterns and then goes back to correlate them with exposures.

The new method also allows for a mix of supervised or unsupervised approaches, controlling or blocking out the effect of known exposures to carcinogens to explore the possible effect of potential unknown factors.

Researchers found that the new supervised method outperformed the unsupervised methodology in terms of prediction accuracy. The supervised methodology had a median area under the curve (AUC) of 0.73 for aging and 0.90 for all other factors, while the unsupervised methodology had a median AUC of 0.57 for aging and 0.77 for all other factors.

“A 0.5 or below AUC means the method is not better than pure chance. The highest value you can get is 1,” said first author Bahman Afsari, PhD, an instructor at the Johns Hopkins Kimmel Cancer until a few months before publication.

The team also revealed what could be the first mutational signatures associated with cancers of obese patients, providing evidence for a mutational mechanism related to obesity and the origination of cancers.

“Obesity is arguably the most important lifestyle factor contributing to cancer, but its mechanism for causing cancer has been unknown,” said Tomasetti. “As cancers of obese patients often do not appear to have an increased number of mutations, it was thought that the mechanism through which obesity increases cancer risk was not via mutations. Our results show that it is, at least in part, mutational.”

The machine learning method also showed that an etiological, or underlying, factor does not always cause the mutational effect on all tissues, a discovery that contrasted with assumptions of the unsupervised methodology.

“Aging yields different mutational signatures in different tissues, and so do smoking and several other environmental exposures,” said co-first author Albert Kuo, Ph.D. candidate at the Johns Hopkins Bloomberg School of Public Health.

“Also, in lungs, the signature for aging and the signature for smoking are very different, but in other tissues, the signature of smoking is relatively similar to the signature for aging, suggesting inflammation as the main mechanism.”

Additionally, the research provided validation for the key role of random mutations – normal mistakes occurring within the DNA of cells during replication – in the development of a cancer.

“Every time a cell divides, it has to duplicate its DNA. As the duplication and repair machinery copies the billions of letters—the molecules that make up our DNA—mistakes are made. It is estimated that there are between three to six DNA mutations occurring every time a cell divides,” said Tomasetti.

“A major source of the mutations that cause cancer appears to be these endogenous processes that have nothing to do with genetic defective genes or harmful exposures.”

With the algorithm, the team determined that 69 percent of the mutations found in cancer patients across all tumor types can be attributed to randomly occurring mutations, indicating the need for a greater focus of effort and resources on early detection.

“If we can’t avoid cancer from occurring, then the next best thing is to find it before it is too late. If we can find a cancer at an early stage, then typically, you can save the life of the patient,” Tomasetti said.

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A team from the Institute of Advanced Study in Science and Technology (IASST) in Guwahati, an autonomous institute of the Department of Science & Technology, Govt of India, has presented the novel deep learning (DL) based quantitative evaluation of oestrogen or progesterone status with the help of Immunohistochemistry (IHC) specimen to grade for prediction of breast cancer.

The scientists developed a classification method based on deep learning (DL) network to evaluate hormone status for prognosis of breast cancer. 

The study by Dr Lipi B Mahanta and her group was done in collaboration with clinicians of B Borooah Cancer Institute, the premier cancer institute of the region. With an enormous prospect for converting to a workable commercial software, this work has been accepted for publication in the pioneer journal Applied Soft Computing.

IHC strain is used as a prognostic marker in breast cancer pathology and involves a special kind of colour staining for identifying malignant nuclei. It possesses different intensity based on which categories are defined in terms of Allred score (ranges 0 to 3) respectively. Scoring systems called Allred and H-score are used by pathologists in the quantification of the immunohistochemical reaction of oestrogen receptor (ER) and progesterone receptor (PR) tissue slides. Hormone receptors, namely oestrogen receptor (ER) and progesterone receptor (PR) contribute to predicting cancer progression and associated risk of late recurrence of the disease.

The team developed an algorithm that indicated whether or not the cancer cells have hormone receptors on their surface. The proposed architecture, namely IHC-Net, can semantically segment the exact positive and negative nuclei from tissue images. Finally, an ensemble method is used, which integrates the decision of three machine learning (ML) models for the final Allred cancer score.

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Researchers say that machine learning could help guide healthcare workers’ treatment decisions for lung cancer patients after developing a model that is 71% more accurate at predicting survival expectancy of patients.

A team of Penn State Great Valley researchers conducted a study in which they developed a deep learning model that is more than 71% accurate in predicting survival expectancy of lung cancer patients, which is significantly better than traditional machine learning models that the team tested which have around a 61% accuracy rate.

Deep learning is a type of machine learning that is based on artificial neural networks, which are generally modelled on how the human brain’s own neural network functions.

Informing patient care

The team say that the information on a patient’s survival expectancy could help guide doctors and caregivers in making better decisions on using medicines, allocating resources, and determining the intensity of care for patients. The machine learning model is able to analyse vast amounts of data and can include information such as types of cancer, tumour size, the speed of tumour growth, and demographic data.

Youakim Badr, associate professor of data analytics, said: “This is a high-performance system that is highly accurate and is aimed at helping doctors make these important decisions about providing care to their patients. Of course, this tool can’t be used as a substitute for a doctor in making decisions on lung cancer treatments.”

According to the researchers this deep learning method may be uniquely suited to tackle lung cancer prognosis because the model can provide the robust analysis necessary in cancer research.

Badr said: “Deep learning is a machine-learning algorithm that makes associations between the data, itself, and the labels that we use to describe the data examples. By making these associations, it learns from the data.”

Robin Qiu, professor of information science and engineering and an affiliate of the Institute for Computational and Data Sciences added that deep learning also offers several advantages for many data science tasks, especially when confronted with data sets that have a large number of records, in this case patients, as well as a large number of features.

“In deep learning we can go deeper, which is why they call it that. In traditional machine learning, you have a simple structure of layers of neural networks. In each layer, you have a group of cells,” he said. “In deep learning, there are many layers of these cells that can be architected into a sophisticated structure to perform better feature transformation and extraction, which gives you the ability to further improve the accuracy of any model.”

In the future, the researchers would like to improve the model and test its ability to analyse other types of cancers and medical conditions.

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A deep learning algorithm could help providers predict survival expectancy in patients with lung cancer, which could help guide treatment decisions.

A deep learning tool was able to accurately predict survival expectancy in patients with lung cancer, potentially leading to more informed care decisions by providers, according to a study published in the International Journal of Medical Informatics.

The study showed that in certain conditions, the deep learning model was more than 71 percent accurate in predicting survival expectancy of lung cancer patients, compared to other machine learning models that performed with about 61 percent accuracy.

The tool can analyze a large amount of data that describe the patients and the disease to understand how a combination of factors affect lung cancer survival periods. These factors include information like types of cancer, size of tumors, speed of tumor growth, and demographic data.

“This is a high-performance system that is highly accurate and is aimed at helping doctors make these important decisions about providing care to their patients,” said Youakim Badr, associate professor of data analytics at Penn State Great Valley. “Of course, this tool can’t be used as a substitute for a doctor in making decisions on lung cancer treatments.”

Information on a patient’s survival expectancy could help guide providers and caregivers make improved decisions on using medicines, allocating resources, and determining the intensity of care for patients.

Researchers noted that deep learning techniques are uniquely suited to address lung cancer prognosis because the technology can provide the comprehensive analysis needed in cancer research.

In deep learning, developers apply a sophisticated structure of multiple layers of artificial neurons. The learning aspect of deep learning comes from how the system learns from connections between data and labels, the team noted.

“Deep learning is a machine-learning algorithm that makes associations between the data, itself, and the labels that we use to describe the data examples,” said Badr. “By making these associations, it learns from the data.”

The structure of deep learning also offers several advantages for many data science tasks, especially in cases involving large datasets.

“It improves performance tremendously. In deep learning we can go deeper, which is why they call it that. In traditional machine learning, you have a simple structure of layers of neural networks. In each layer, you have a group of cells,” said Robin G. Qiu, professor of information science and engineering and an affiliate of the Institute for Computational and Data Sciences.

“In deep learning, there are many layers of these cells that can be architected into a sophisticated structure to perform better feature transformation and extraction, which gives you the ability to further improve the accuracy of any model.”

Researchers analyzed data from the Surveillance, Epidemiology, and End Results (SEER) program. The SEER dataset is one of the biggest and most comprehensive databases on the early diagnosis information for cancer patients in the US. The program’s cancer registries cover almost 35 percent of US cancer patients.

“One of the really good things about this data is that it covers a large section of the population and it’s really diverse,” said Shreyesh Doppalapudi, a graduate-student research assistant and first author of the paper.

“Another good thing is that it covers a lot of different features, which you can use for many different purposes. This becomes very valuable, especially when using machine learning approaches.”

When the team compared several deep learning approaches to traditional machine learning models, the deep learning approaches performed much better than traditional machine learning methods.

The deep learning technique enabled researchers to find associations in the SEER dataset, which includes about 800,000 to 900,000 entries – something that would have been incredibly challenging without the assistance of AI.

“If it were only three fields I would say it would be impossible — and we had about 150 fields,” said Doppalapudi. “Understanding all of those different fields and then reading and learning from that information, would be impossible.”

Going forward, researchers will aim to improve the model and test its ability to analyze other types of cancers and medical conditions.

“The accuracy rate is good, but it’s not perfect, so part of our future work is to improve the model,” said Qiu.

Researchers also plan to connect with domain experts on specific cancers and medical conditions.

“In a lot of cases, we might not know a lot of features that should go into the model,” said Qiu. “But, by collaborating with domain experts, they could help us collect important features about patients that we might not be aware of and that would further improve the model.”

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A team of researchers at the James A. Haley Veterans’ Hospital in Tampa, Florida, is revolutionizing the way cancer is documented by enlisting the help of a computer to diagnose the disease in one of the largest patient populations in the nation: veterans.

Sophisticated artificial intelligence is capable of drastically altering how cancer is diagnosed and treated by learning to distinguish imagery of tissue containing cancerous cells from pictures of healthy tissue, a recent study in the Federal Practitioner journal claims.

“Based on a set of images selected to represent a specific tissue or disease process, the computer can be trained to evaluate and recognize new and unique images from patients and render a diagnosis,” the study’s authors wrote.

To test machine learning software, researchers uploaded hundreds of microscopic images of commonly diagnosed forms of the disease, such as lung or colon cancer, along with pictures of non-cancerous cells. At the conclusion of the test, the software — both Google- and Apple-based versions were tested — not only distinguished cancerous cells from non-cancerous tissue with a success rate of better than 90 percent, but it also indicated the exact form of cancer it was analyzing.

The ability of user-friendly machine learning software to learn and efficiently perform traditional human tasks in less time will alleviate some of the demand on medical practitioners who are already being stretched thin, the authors claim.

By coupling AI with a growing list of telehealth options, specialists have the potential to reach patients from anywhere in the world. Greater accessibility would especially benefit the millions of patients in the VA’s healthcare system, many of whom live in remote, rural areas where specialists or facilities needed to treat unique diseases are scarce at best.

A collaborative doctor-AI system can also diminish patient wait times and effectively eliminate the time-consuming paperwork analysis that has always bogged down practitioners. What it won’t do, according to one of the study’s authors, is replace its Homo sapien counterparts.

“Our ultimate goal would be to create programs that can be rolled out in the entire VA system so that pathologists who are working solo, or maybe there are two pathologists in some small VAs, would have the benefit of having something that is helping them become more productive, help them prioritize the workload and improve quality,” Dr. Andrew Borkowski said in a VA release.

And while the hope of machine learning enthusiasts is to eventually apply AI-assisted healthcare on a global scale, early testing using the VA’s expansive patient base allows for the mining of data from a seemingly limitless source.

The myriad imagery generated from the nearly 50,000 cancer diagnoses of veterans each year, for example, will enable AI software to analyze more data, learn faster, and expand application to other demographics and diseases at a pace other healthcare systems cannot match.

All this is not to say there won’t be obstacles to overcome before AI can be considered entirely viable — ensuring the impeccable accuracy of its decision-making paramount among them.

In 2019, Google-run AI software was fed hundreds of images and tested to determine whether it could predict the early onset of a deadly kidney disease. Two of every three AI-generated results yielded false positives. Significant diagnostic errors like that can be detrimental to practitioners who then follow up on phantom diseases using valuable time that could be spent treating patients in dire need, Mildred Cho, associate director of the Stanford Center for Biomedical Ethics, told WUSF News.

Continued success in machine learning trials like the one at the James A. Haley Veterans’ Hospital, however, bode well for AI’s future implementation into healthcare. Researchers hope continuously evolving software, such as Apple-produced AI that is now capable of recognizing images that have been rotated, flipped, or cropped, will help alleviate a glaring industry-wide trend.

The “number of pathologists in the U.S. is dramatically decreasing, and many other countries have marked physician shortages, especially in fields of specialized training such as pathology,” the study’s authors wrote. “These models could readily assist physicians in underserved countries and impact shortages of pathologists elsewhere by providing more specific diagnoses in an expedited manner.”

Future application of machine learning AI, the study concluded, will be immeasurably beneficial in diagnosing and documenting everything from various forms of cancer to non-cancerous diseases, brain hemorrhages, blood disorders, infections, and inflammatory issues.

“The potential of these technologies to improve health care delivery to veteran patients seems to be limited only by the imagination of the user.”

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To better study the outcomes and costs of caring for cancer patients and emphasize value, oncologists at Hackensack Meridian Health’s John Theurer Cancer Center, in New Jersey, teamed with data scientists and engineers eight years ago to create their own analytics program to mine patients’ electronic health records for real-world data.

Since then, oncologists have used their cancer outcomes tracking and analytics (COTA) program—which has been spun off into a private company—in numerous research efforts to track metrics from how many patients receive proper workups to how various cancer treatment regimens perform based on disease characteristics.

When building the system, the team embraced the concept of barcodes, said oncologist Stuart L. Goldberg, MD, the chief of the center’s Division of Outcomes and Value Research. The code on a can of tomato soup, for example, contains information on the item’s price, size and manufacturer. This enables the store to accurately know its inventory and what’s selling, and to control quality.

“Although the barcode system revolutionized the retail industry, medicine has lagged behind with a broad billing code system that fails to capture important characteristics needed to distinguish complex diseases, such as cancer,” Dr. Goldberg said.

Linking Disease’s Many Aspects

Dr. Goldberg and his team wanted to use COTA to establish barcodes for cancer that contain prognostic information such as tumor size, genetic characteristics, and whether the cancer metastasized. They set up the system to access doctors’ notes in the electronic health record to “barcode” the disease, allowing the user to determine how many patients had early-stage disease or a particular genetic subtype, for example, with a few clicks on the computer.

“COTA is currently looking at our charts, coding the diseases and giving our oncologists back organized information, so we can know which patients we are treating with a particular regimen, and we can link that with patient outcomes and costs,” Dr. Goldberg said.

One research focus has been looking to streamline therapies so all patients with the same subtypes of cancer receive the same care. They have submitted for publication a report on data from over 4,000 breast, colon and lung cancer patients that shows how unwarranted variations in care drive up costs without noticeable clinical benefits. 

Another recent project that made headlines was a partnership with IBM Watson for Oncology, in which the COTA program fed barcoded information about breast cancer patients to Watson, which, in turn, used that information to generate treatment recommendations. The team separately asked breast cancer specialists what they recommended. About 90% of the time, the experts and Watson for Oncology agreed on the therapies. In contrast, among oncologists who did not specialize in breast cancer, concordance fell to 75%.

“Watson for Oncology may be helpful in the community setting, where you have generalists who could benefit from added decision support,” Dr. Goldberg said. “I’d love to think that every doctor is perfect, but unfortunately the field of oncology changes so fast; and unless you’re really an expert in that one disease, it’s hard to keep up.”

Next on the horizon is looking at methods to better structure physician notes to enable COTA to conduct faster data extraction and generation of codes, he said. This could enable insurers to receive barcoded, accurate, detailed documentation sooner for preauthorizations “and eliminate time-consuming faxing of information back and forth between the insurer and the practice,” he said.

“The days of a handwritten, unintelligible paper chart that gets lost are over,” Dr. Goldberg said. “We have everything in the computer, and it is a rich source of real-world data. Now we have to mine that data to learn from every patient’s experience, so that we know what we are doing, what’s working, and what it costs. That will lead to value.”

Provider Discretion Preserved

Data mining as described here “can more easily help us determine best options for more individualized patient care,” commented Robert Mancini, PharmD, the bone marrow transplant pharmacy program coordinator at St. Luke’s Health System, in Boise, Idaho. “Right now, most guidelines are fairly broad and don’t always take into account things such as comorbidities or genetic characteristics in a detailed manner, which can impact responses to therapy.”

Oncology pathways that allow more patient-specific decision making based on specific disease types and stages are not universally accepted or agreed upon by clinicians, he added.

However, analytics programs need to integrate with—not replace—clinical decision making, Dr. Mancini cautioned. “While it is good to maximize outcomes and minimize cost, we want to be careful about taking away critical thinking and provider discretion.”

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While the study is not a pioneering work—NYU research had already established similar results—what makes Google’s work unique is the fact that it was conducted across two countries with a larger sample. More important, as against artificial lab comparison, this one compares performance with real-world diagnoses.

When Google, in 2016, announced that its DeepMind technology was able to detect diseases from eye scans, many had heralded it as the age of AI replacing the doctors. While Google has deployed its systems across the world since, its recent results showcase a step taken forward for medical technology. The company, in a research published in Nature, highlighted that its technology has been ourtperforming radiologists in breast cancer detection using mammography scans. While the study is not a pioneering work—NYU research had already established similar results—what makes Google’s work unique is the fact that it was conducted across two countries with a larger sample. More important, as against artificial lab comparison, this one compares performance with real-world diagnoses.

It was found that, for US and UK patients, AI reduced false negatives—test results wrongly categorised normal—by 9.4% and 2.7%, while for false positives, where cancer is erroneously detected, the reduction was 5.7% and 1.2%, respectively. Given that radiologists can’t detect cancer in one in five cases, this is a marked improvement. While most tests were done using the same equipment, this facility may not be available in a developing economy, the results still show promise. If combined with human intelligence, Google can bring down errors by even a 50%, it would save lot of lives. According to WHO, breast cancer impacts 2.1 million women each year.

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June 12, 2019 – Using deep learning technology, researchers from Massachusetts Institute of Technology (MIT) and Massachusetts General Hospital (MGH) were able to predict women’s future risk of breast cancer development more accurately than when they used traditional methods, according to a study published in Radiology.

Current models use factors like genetics and family history to predict risk, but these tools often fall short. Breast density is an independent risk factor for predicting breast cancer risk, but it’s based on subjective assessment that can vary among radiologists.

Researchers developed a deep learning model that could standardize and automate breast density measurements.

“There’s much more information in a mammogram than just the four categories of breast density,” said study lead author Adam Yala, PhD candidate at MIT in Cambridge, Mass. “By using the deep learning model, we learn subtle cues that are indicative of future cancer.”

The team compared three different risk assessment models. The first used traditional risk factors, and the second used deep learning that evaluated the mammogram alone. The third was a hybrid method that used both the mammogram and traditional risk factors into the deep learning model.

Researchers trained and tested the models on nearly 90,000 screening mammograms from about 40,000 women and found that both deep learning models performed with greater accuracy than the traditional model.

When using the deep learning models to predict women’s risk based on breast density, the team found that patients with non-dense breasts and model-assessed high risk had 3.9 times the cancer incidence of patients with dense breasts and model-assessed low risk. These advantages held across different subgroups of women.

“Unlike traditional models, our deep learning model performs equally well across diverse races, ages and family histories,” said Regina Barzilay, PhD, an AI expert and professor at MIT. “Until now, African-American women were at a distinct disadvantage in having accurate risk assessment of future breast cancer. Our AI model has changed that.”

At MGH, clinicians are already using artificial intelligence to assist with breast density measurements. Researchers are tracking its performance in the clinic and working to refine how they communicate risk information to women and their primary care physicians.

“A missing element to support more effective, more personalized screening programs has been risk assessment tools that are easy to implement and that work across the full diversity of women whom we serve,” said Constance Lehman, MD, PhD, chief of breast imaging at MGH and professor of radiology at Harvard Medical School.

“We are thrilled with our results and eager to work closely with our health care systems, our providers and, most importantly, our patients to incorporate this discovery into improved outcomes for all women.”

Deep learning has proven itself to be a reliable support tool for cancer care. In 2018, a team at Google developed a deep learning tool that could detect metastasized breast cancer with 99 percent accuracy.

Researchers at Case Western Reserve University also built a model that achieved 100 percent accuracy when identifying invasive forms of breast cancer in pathology images.

“If the network can tell which patients have cancer and which do not, this technology can serve as triage for the pathologist, freeing their time to concentrate on the cancer patients,” Anant Madabushi, a biomedical engineering professor at Case Western Reserve and co-author of the study, said at the time.

“To put this in perspective, the machine could do the analysis during ‘off hours,’ possibly running the analysis during the night and providing the results ready for review by the pathologist when she/he were to come into the office in the morning.”

The research from MGH and MIT builds on these efforts, and further shows the potential for deep learning to transform cancer care and diagnosis.

“There’s a very large amount of information in a full-resolution mammogram that breast cancer risk models have not been able to use until recently,” Yala said. “Using deep learning, we can learn to leverage that information directly from the data and create models that are significantly more accurate across diverse populations.”

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One of the major challenges faced in treating cancer is the constantly changing nature of tumours hence proposed treatment at a particular time may not be effective because tumours become resistant to drugs or proposed treatment.

Artificial Intelligence in a new development has come in useful in predicting how cancers will progress and evolve, and this will go a long way in helping doctors propose the most effective treatment for each patient.

A team led by the Institute of Cancer Research London[ICR] and the University of Edinburgh developed a new technique known as Revolver [Repeated Evolution of Cancer]

This technique notes and selects the system in DNA mutation within cancers and uses the information to predict future genetic changes.

Cancer evolves unpredictably but if doctors can predict how the tumours evolve, they could step in and stop it before it advances or develops and increase the patient’s chances of survival.

The team also discovered a link between a certain pattern of recurrent tumour mutations and survival outcomes.
This indicates that repeating patterns of DNA mutations could be used as a means of prognosis, helping to build future treatment.

The research team developed a new machine learning technique which identifies patterns in genetic mutations that happen in tumours and applying the patterns to predict another.

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