AI and Breast Cancer: How Artificial Intelligence is Advancing Detection & Treatment

The Ongoing Fight Against Breast Cancer: A Four-Decade Awareness Journey

For the past four decades, each October has been dedicated to Breast Cancer Awareness Month, a period focused on increasing understanding of the most commonly diagnosed cancer globally. This disease tragically claims nearly three-quarters of a million lives annually.

A History Shrouded in Silence

Although documented instances of breast cancer date back to ancient Egypt, the condition was historically considered taboo for centuries. Women were often expected to endure their suffering privately and with perceived “dignity.”

This prevailing stigma significantly hindered academic investigation, resulting in breast cancer remaining a comparatively understudied illness for a considerable period. Throughout much of the 20th century, treatment options for women with breast cancer were largely limited to radiation therapy and/or surgery – frequently involving radical procedures that offered minimal benefit while causing disfigurement. Meanwhile, advancements were being made in the treatment of other cancers.

A Shift Driven by Advocacy

Breast cancer mortality rates remained largely stagnant from the 1930s to the 1970s. However, a dedicated effort by feminist and women’s liberation movements brought about a crucial change. They successfully advocated for elevating the study and treatment of breast cancer to a more prominent position within predominantly male-dominated medical institutions and research facilities. This led to a rapid transformation in treatment approaches within a single generation.

Improved Survival Rates

In the 1970s, a woman receiving a breast cancer diagnosis faced approximately a 40% chance of surviving the following decade. Today, that probability has nearly doubled, a direct result of innovative drugs, advanced screening techniques, and less invasive, more effective surgical procedures.

The Power of Early Detection

A cornerstone of this improvement has been a strong emphasis on early diagnosis. Identifying breast cancer in its initial stages significantly enhances treatment efficacy. Artificial intelligence is now playing an increasingly vital role in this process.

This year, the National Health Service (NHS) in Britain initiated a study exploring the potential of AI in breast cancer screening. While designed to assist, not replace, human doctors, this technology aims to address a critical shortage of radiographers – with an estimated 2,000 more needed to address the backlog of scans resulting from the pandemic.

AI-Powered Startups

Several startups are also leveraging AI to address this shortage. Kheiron Medical Technologies, based in Britain, intends to utilize AI to screen half a million women for breast cancer. Spain’s the Blue Box is developing a device capable of detecting breast cancer from urine samples. Niramai, an Indian company, is creating a low-cost tool to facilitate screening for large populations in rural and semi-urban areas.

Predicting Relapse Risk

Equally important to improving patient outcomes is the ability to identify individuals at high risk of cancer recurrence. Approximately one in ten breast cancer patients will experience relapse after initial treatment, which negatively impacts their survival chances.

Historically, identifying these patients has been challenging. However, my team, in collaboration with Gustave Roussy, a French cancer hospital, has developed an AI tool capable of accurately identifying 8 in 10 patients at high risk of relapse. This allows for earlier intervention and targeted treatment, while also reducing unnecessary checkups for lower-risk patients.

Addressing Data Privacy Concerns

Patient data privacy often presents a challenge to accelerated research. Hospitals are understandably cautious about sharing data externally, and pharmaceutical companies are reluctant to share proprietary information with competitors. However, AI is helping to overcome these obstacles, enabling the faster, safer, and more cost-effective development of new treatments.

Federated Learning: A Collaborative Approach

Federated learning, an innovative AI technique, allows for training on data from multiple institutions without the data ever leaving the hospitals. This approach is currently being utilized across Europe to provide researchers with access to essential data that was previously inaccessible.

Furthermore, AI will be instrumental in deepening our understanding of why the most aggressive forms of breast cancer exhibit resistance to certain drugs. This knowledge will facilitate the development of new, targeted therapies that can more effectively differentiate between healthy and cancerous cells than traditional chemotherapy.

The Human Element in Healthcare

While AI’s influence is growing, it’s crucial to remember that healthcare is fundamentally a human endeavor. No algorithm can provide the comfort a patient needs during difficult times, nor can a machine inspire the resilience required to overcome the disease.

As doctors, we understand that treating illness involves understanding the patient as much as understanding the affliction itself. Clinician empathy is linked to increased patient satisfaction and reduced distress, encouraging patients to adhere to challenging treatment plans. Fortunately, the AI technologies assisting in breast cancer treatment are designed to enhance and empower doctors.

A Future of Hope

Breast cancer is no longer a “unspeakable” disease for the millions diagnosed each year. The widespread use of pink ribbons each October symbolizes the progress made in our fight against this age-old enemy – a battle we are increasingly winning.

While complete eradication of breast cancer may not be achievable, the potential for AI to diagnose patients earlier and accelerate the development of treatments offers hope. It is conceivable that, in the coming decades, we may no longer require a dedicated Breast Cancer Awareness Month.