Grand Challenge Research Harnesses AI to Fight Breast Cancer

Breast cancer has recently overtaken lung cancer to become the most common cancer globally, according to the World Health Organization. Advancing the fight against breast cancer, the BreastPathQ Challenge was launched at SPIE Medical Imaging 2019 to support the development of computer-aided diagnosis for assessing breast cancer pathology.

BreastPathQ Challenge participants were tasked with developing an automated method for analyzing microscopy images of breast tissue and ranking them according to their tumor cell content, to provide a reliable assessment score. As reported in SPIE's Journal of Medical Imaging (JMI), the challenge produced encouraging results that indicate a path toward integrating artificial intelligence (AI) to streamline clinical assessment of breast cancer.

Medical imaging for neoadjuvant treatment

Treatment for large or aggressive breast cancers has often turned to mastectomy as the most reliable therapy. However, therapy known as "neoadjuvant treatment" can result in reduced tumor size, density, and spread, making patients candidates for breast-conserving surgery rather than mastectomy.

Medical imaging allows doctors to assess the effects of neoadjuvant treatment. While the processes of analyzing medical images for cancer detection are typically performed manually and rely on expert interpretation of complex tissue structures, machine-learning algorithms for identifying cancer may increase the reliability and efficiency of those processes. In addition to reducing variability, which is inherent to human pathologists, fully automated methods like these are expected to increase the speed of image analysis.

Intensive focus, international effort

A total of 39 teams from 12 different countries worldwide engaged in the BreastPathQ Challenge. A total of 100 algorithms were developed, validated, and tested. Teams were able to compare their algorithms with those of others from academia, industry, and government, as structured by the Grand Challenge framework, which requires a shared set of source data.

Most of the teams used an ensemble of machine-learning algorithms instead of limiting themselves to a single AI architecture. Top algorithms performed at levels comparable to the pathologists who provided the reference standards for the study, and the best performing algorithm slightly surpassed the scores of the pathologists. The algorithms generally performed well on easier patches of images but struggled on the difficult patches - those for which AI would be especially beneficial to pathologists.

The BreastPathQ Challenge was successful because the organizing committee brought together experts in multiple fields. According to Nicholas Petrick, deputy director for the Division of Imaging, Diagnostics and Software Reliability in the US FDA Center for Devices and Radiological Health, and representative for the BreastPathQ Challenge Group, advance collaborative groundwork meant that participants were able to move quickly and efficiently to address the task, access the data set, and develop their algorithms.

Petrick N, Akbar S, Cha KH, Nofech-Mozes S, Sahiner B, Gavrielides MA, Kalpathy-Cramer J, Drukker K, Martel AL; BreastPathQ Challenge Group.
SPIE-AAPM-NCI BreastPathQ challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment.
J Med Imaging (Bellingham). 2021, doi: 10.1117/1.JMI.8.3.034501

Most Popular Now

AI Helps Physicians Better Assess the Ef…

In a small but multi-institutional study, an artificial intelligence (AI)-based system improved providers' assessments of whether patients with bladder cancer had complete response to chemotherapy before a radical cystectomy (bladder...

Smartwatches and Fitness Bands Reveal In…

A new digital health study by researchers at Scripps Research shows how data from wearable sensors, such as smartwatches and fitness bands, can track a person’s physiological response to the...

AI may Detect Earliest Signs of Pancreat…

An artificial intelligence (AI) tool developed by Cedars-Sinai investigators accurately predicted who would develop pancreatic cancer based on what their CT scan images looked like years prior to being diagnosed...

Open Call U4H-2022-PJ2: Call for Proposa…

The Ukraine crisis has an unprecedented impact on the mental health of the displaced people in the EU coming from Ukraine. The conflict and experiences of people in war zones...

AI Reduces Miss Rate of Precancerous Pol…

Artificial intelligence reduced by twofold the rate at which precancerous polyps were missed in colorectal cancer screening, reported a team of international researchers led by Mayo Clinic. The study is...

Medical Valley EMN & Volitan Global …

The two healthcare innovation experts Medical Valley EMN and Volitan Global strengthen their existing inbound- and outbound activities through a strategic partnership. The aim is to offer companies access to...

DMEA - Connecting Digital Health Opens w…

26 - 28 April 2022, Berlin, Germany. What plans does the new federal government have concerning the digital transformation of the healthcare sector? What are the initial experiences of doctors regarding...

AI can Predict Probability of COVID-19 v…

Testing shortages, long waits for results, and an over-taxed health care system have made headlines throughout the COVID-19 pandemic. These issues can be further exacerbated in small or rural communities...

Using AI to Detect Cancer from Patient D…

A new way of using artificial intelligence to predict cancer from patient data without putting personal information at risk has been developed by a team including University of Leeds medical...

Oulu University Hospital Expands Partner…

Siemens Healthineers and Oulu University Hospital in Finland have entered a strategic partnership for the next ten years, adding to an existing radiotherapy collaboration to jointly expand and modernize the...

Positive Conclusion to DMEA - Connecting…

26 - 28 April 2022, Berlin, Germany. After three days DMEA, Europe's leading digital health event, came to a successful conclusion - with around 11,000 visitors, more than 500 exhibitors and...

AI-Enabled ECGs may Identify Patients at…

Atrial fibrillation, the most common cardiac rhythm abnormality, has been linked to one-third of ischemic strokes, the most common type of stroke. But atrial fibrillation is underdiagnosed, partly because many...