Artificial intelligence to now help diagnose breast cancer

Agencies
August 10, 2019

Washington D.C., Aug 10: Researchers discovered an artificial intelligence system that could help pathologists read biopsies more accurately and to better detect and diagnose breast cancer. 

The new system, described in a study published in the journal 'JAMA Network Open,' helped interpret medical images used to diagnose breast cancer that can be difficult for the human eye to classify, and it does so nearly as accurate or better as experienced pathologists.

"It is critical to get a correct diagnosis from the beginning so that we can guide patients to the most effective treatments," said Dr. Joann Elmore, the study's senior author and a professor of medicine at the David Geffen School of Medicine at UCLA.

A 2015 study led by Elmore found that pathologists often disagree on the interpretation of breast biopsies, which are performed on millions of women each year.

That earlier research revealed that diagnostic errors occurred in about one out of every six women who had ductal carcinoma in situ (a noninvasive type of breast cancer), and that incorrect diagnoses were given in about half of the biopsy cases of breast atypia (abnormal cells that are associated with a higher risk for breast cancer).

"Medical images of breast biopsies contain a great deal of complex data and interpreting them can be very subjective. Distinguishing breast atypia from ductal carcinoma in situ is important clinically but very challenging for pathologists. Sometimes, doctors do not even agree with their previous diagnosis when they are shown the same case a year later," said Elmore, who is also a researcher at the UCLA Jonsson Comprehensive Cancer Center.

The scientists reasoned that artificial intelligence could provide more accurate readings consistently because by drawing from a large data set, the system can recognise patterns in the samples that are associated with cancer but are difficult for humans to see.

The team fed 240 breast biopsy images into a computer, training it to recognise patterns associated with several types of breast lesions, ranging from benign (noncancerous) and atypia to ductal carcinoma in situ, or DCIS, and invasive breast cancer. Separately, the correct diagnoses for each image were determined by a consensus among three expert pathologists.

To test the system, the researchers compared its readings to independent diagnoses made by 87 practising U.S. pathologists. While the artificial intelligence program came close to performing as well as human doctors in differentiating cancer from non-cancer cases, the AI program outperformed doctors when differentiating DCIS from atypia, considered the greatest challenge in breast cancer diagnosis.

The system correctly determined whether scans showed DCIS or atypia more often than the doctors; it had sensitivity between 0.88 and 0.89, while the pathologists' average sensitivity was 0.70. (A higher sensitivity score indicates a greater likelihood that diagnosis and classification are correct.)

"These results are very encouraging. There is low accuracy among practising pathologists in the U.S. when it comes to the diagnosis of atypia and ductal carcinoma in situ, and the computer-based automated approach shows great promise," Elmore said.

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Agencies
March 11,2020

With the sales of chicken and mutton going down due to the coronavirus scare, it is the humble 'Kathal' (jackfruit) is emerging as an acceptable alternative.

'Kathal' is now selling at ₹120 per kilogram -- an increase of more than 120 per cent over the normal ₹50 per kilogram.

The jackfruit, in fact, is now priced higher than chicken which is selling at ₹80 per kilogram due to poor demand.

"It is better having a 'Kathal' biryani instead of a mutton biryani. It tastes reasonably good. The only problem is that 'Kathal' has been sold out in the vegetable market and is difficult to find," said Purnima Srivastava whose family savours non-vegetarian food on a regular basis.

The corona scare has hit poultry business so hard and the Poultry Farm Association recently organized a Chicken Mela in Gorakhpur to dispel the misconception that birds are carriers of the deadly virus.

"In fact, we gave away plateful of chicken dishes for Rs 30 to encourage people to savour the delicacies. We cooked one thousand kilograms of chicken for the Mela and the entire stock was sold out," said Vineet Singh, head of the Poultry Farm Association.

However, the Mela did not do much to dispel the fears about chicken, mutton or fish consumption amid the virus outbreak.

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News Network
January 31,2020

Jan 31: Cervical cancer could be eliminated worldwide as a public health issue within the next 100 years, according to two studies which may lead to better strategies for screening and vaccination against the malignant disease.

According to the studies, published in the journal The Lancet, more than 74 million cervical cancer cases, and 60 million deaths could be averted, and the disease eliminated in the 78 countries which have the highest disease burden.

The researchers, including those from Laval University in Canada, said cervical cancer is the second most frequent cancer among women in low-income and lower-middle-income countries (LMICs) with 2,90,000 (51 per cent) of the 5,70,000 new cases worldwide reported in women living in LMICs.

In the current studies, the scientists used the WHO draft strategy of cervical cancer elimination which defines plans for vaccination against the disease's causative agent, the human papillomavirus (HPV).

These plans, they explained, call for 90 per cent of girls to be vaccinated against HPV by 2030, and for 70 per cent of women to be screened for cervical cancer once or twice in their lifetime.

About 90 per cent of women with precancerous lesions, or cervical cancer are also advised to receive appropriate treatment, according to the WHO draft strategy, the scientists said.

In the second study, the research team analysed the impact of three elements of the WHO strategy on deaths from cervical cancer -- modelling the impact of scaling up cancer treatment, as well as vaccination and screening

"Our findings emphasise the importance of acting immediately to combat cervical cancer on all three fronts," said Karen Canfell from the University of Sydney in Australia, who co-led both the studies.

"In just 10 years, it's possible to reduce deaths from the disease by a third and, over the next century, more than 60 million women's lives could be saved. This would represent an enormous gain in terms of both quality of life, and lives saved," Canfell said.

By adding the two screening tests, and with the treatment of precancerous cervical lesions, cases of the cancer may drop by 97 per cent, and 72 million cervical cancer cases could be averted over the next century, the researchers said.

Scaling-up of appropriate cancer treatment could avert 62 million cervical cancer deaths, the study noted.

"For the first time, we've estimated how many cases of cervical cancer could be averted if WHO's strategy is rolled out and when elimination might occur," said Marc Brisson, study co-author from Laval University.

"Our results suggest that to eliminate cervical cancer it will be necessary to achieve both high vaccination coverage, and a high uptake of screening and treatment, especially in countries with the highest burden of the disease," Brisson added.

Based on the results of the studies, WHO's cervical cancer elimination strategy has been updated which will be presented for adoption at the World Health Assembly in May 2020, the scientists noted.

"If the strategy is adopted and applied by member states, cervical cancer could be eliminated in high income countries by 2040, and across the globe within the next century, which would be a phenomenal victory for women's health," Brisson said.

"However, this can only be achieved with considerable international financial and political commitment, in order to scale-up prevention and treatment," he added.

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News Network
February 26,2020

New York, Feb 26:  A new wearable sensor that works in conjunction with artificial intelligence (AI) technology could help doctors remotely detect critical changes in heart failure patients days before a health crisis occurs, says a study.

The researchers said the system could eventually help avert up to one in three heart failure readmissions in the weeks following initial discharge from the hospital and help patients sustain a better quality of life.

"This study shows that we can accurately predict the likelihood of hospitalisation for heart failure deterioration well before doctors and patients know that something is wrong," says the study's lead author Josef Stehlik from University of Utah in the US.

"Being able to readily detect changes in the heart sufficiently early will allow physicians to initiate prompt interventions that could prevent rehospitalisation and stave off worsening heart failure," Stehlik added.

According to the researchers, even if patients survive, they have poor functional capacity, poor exercise tolerance and low quality of life after hospitalisations.

"This patch, this new diagnostic tool, could potentially help us prevent hospitalizations and decline in patient status," Stehlik said.

For the findings, published in the journal Circulation: Heart Failure, the researchers followed 100 heart failure patients, average age 68, who were diagnosed and treated at four veterans administration (VA) hospitals in Utah, Texas, California, and Florida.

After discharge, participants wore an adhesive sensor patch on their chests 24 hours a day for up to three months.

The sensor monitored continuous electrocardiogram (ECG) and motion of each subject.

This information was transmitted from the sensor via Bluetooth to a smartphone and then passed on to an analytics platform, developed by PhysIQ, on a secure server, which derived heart rate, heart rhythm, respiratory rate, walking, sleep, body posture and other normal activities.

Using artificial intelligence, the analytics established a normal baseline for each patient. When the data deviated from normal, the platform generated an indication that the patient's heart failure was getting worse.

Overall, the system accurately predicted the impending need for hospitalization more than 80 per cent of the time.

On average, this prediction occurred 10.4 days before a readmission took place (median 6.5 days), the study said.

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