A team of engineers from the University of Cologne’s Faculty of Medicine and University Hospital Cologne have introduced an AI model that can help detect lung cancer tissue in minutes. The new system combines the most extensive data set related to the disease available and has shown some promising results. Here’s everything you need to know about how AI is poised to enhance lung cancer diagnosis in the near future.
Lung cancer
Lung cancer claimed the lives of an estimated 1.3M people in 2023. This disease is one of the most deadly in the world and most common. Within the lung cancer types, Non-small cell lung cancer (NSCLC) accounts for more than 80% of all lung cancers. This variety of lung cancer introduces malignant tumors into the lung tissue over time.
Lung cancer patients must undergo intensive and draining treatments to remove the tumors before they destroy surrounding tissues. Even with diagnosis and initial treatment averaging +$68k, there’s still a high mortality rate. Thus, diagnosis is seen as only second to prevention.
Pathological Examination
Pathological examinations are how oncologists discover lung cancer tissue. This process requires healthcare professionals to gather hematoxylin and eosin (H&E)-stained tissue samples. Oncologists then review these samples to determine if tumor cells are present. Additionally, they use your data and genetics to tailor an effective treatment.
Digital Transformation
The first steps of tissue gathering have remained the same in oncology for decades. However, the way and means by which the data gets processed have migrated to a digital format. Digital pathology platforms eliminated the need for researchers to be peering at cells through microscopes. Now, they use computer monitors.
Digitizing pathology brought some serious benefits, including the ability to integrate software into the discovery process. Today, most experts use some form of digital lung tissue analysis to determine your state. In the coming years, artificial intelligence will replace manually operated software systems as the primary way of determining lung cancer’s presence in tissues.
New Opportunities
Artificial intelligence models can leverage the vast array of histological images and extract additional information that human reviewers can’t capture. As such, there’s a strong push to create more effective and accessible AI-powered pathological systems.
AI Lung Cancer Study
The study “Next generation lung cancer pathology: development and validation of diagnostic and prognostic algorithms” published in the journal Cell Reports Medicine unveils new AI algorithms and computational pathology platform designed specifically for NSCLC diagnosis.
The study demonstrates a combination of new AI foundation models and represents the largest and most relevant data set used to date. The system integrates a detailed multi-class tissue dataset that includes whole-slide images with vital details such as lung adenocarcinoma and squamous cell carcinomas. Notably, the AI model integrated +4k slides from 1,527 patients and was derived from an international cohort of lung cancer research providers.
Test
The testing phase of the research involved comparing tissue sample results with expert pathologist opinions to ensure quality. The team was keen on using only explainable, independent, capable prognostic parameters derived from H&E-stained tissue samples, which made it easier to confirm results.
Source – Cell Report Medicine
Four AI models were used in the experiment. Each AI algorithm was designed to examine and determine different classes including epithelial tumor component, tumoral stroma, necrotic debris, and mucin. The AI system reviewed the live data and compared it to tertiary lymphoid structure and necrosis assessments within the model seeking similarities.
Lung Cancer Study Results
The results revealed that the algorithm was highly accurate and faster than other methods of determining lung cancer tumors. The team demonstrated .89 accuracy, with many of the inaccuracies falling under optical issues related to pixels rather than the AI algorithm’s detection capabilities.
Benefits
An AI-powered lung cancer detection system brings several benefits to the market. For one, these low-cost alternatives can be used in remote regions where larger, more specialized equipment and professionals are not available. As such, they could help create a more balanced and accessible treatment process.
Automated Analysis
One of the main benefits of the AI system is that it’s completely automated. Tissue samples are scanned, shown, tested, and treatment recommendations are made by the system. By reducing diagnosis times, patients can lower treatment needs and costs.
New Information
Another major reason why this study has many professionals excited is that it opens the door for new data collection methods to be derived. AI algorithms are getting more capable of determining hard-to-see patterns and connections within data sets. As such, this system will be able to continually learn from old and new information collected from patients, improving its capabilities.
Millions of lung cancer tissue samples have been taken over the years this disease has been researched. This data may unlock some clues into future prevention methods once imputed into a larger AI model that can determine hard-to-detect patterns and connected occurrences.
Improved Treatments
Another major benefit is better treatments. This system enables healthcare professionals to create optimized and personalized treatments for their patients in record time. Lung cancer diagnosis is vital in preventing the spread of the disease and reducing mortality rates in patients. In the future, these systems could be placed in your home or even rented to individuals or small clinics. This maneuver would open the door for global adoption while reducing misdiagnosis, the need to travel, and expenses for all parties involved.
Lung Cancer Study Researchers
The research team for the project was led by Dr Yuri Tolkach and Professor Dr Reinhard Büttne from the Institute of General Pathology and Pathological Anatomy at University Hospital Cologne. The project was made possible through funding grants from the North Rhine-Westphalia state, and the Federal Ministry of Education and Research of Germany, and the Wilhelm Sander Foundation.
Two Companies that Can Benefit from the AI Lung Cancer Study
Several major pharmaceutical firms could benefit greatly from integrating this new AI model to improve their diagnosis and treatment processes. This low-cost alternative would be easy to introduce effectively into their current business model. Additionally, these firms could integrate their proprietary research data to improve the AI’s capabilities even further. Here are two companies that should consider this maneuver in the coming weeks.
1. Sanofi 
Sanofi entered the market in 1973 as an international pharmaceutical firm. This France-based operation is a pioneer in the field of cancer diagnosis and treatments. The company offers a variety of products including molecular oncology, immuno-oncology, and genomic medicine platforms. Sanofi has seen great success due to its launch of multiple treatments.
Sanofi saw a major boost in price recently as it revealed that early trials of ibrutinib multiple sclerosis (MS) treatment went well. This news wasn’t all roses as the following two tests didn’t give the company the results they intended. However, it did build trader confidence as the research is heading in the right direction. Currently, the firm has +13 candidates in clinical trials. If approved, these options will join the company’s many other offerings, designed to treat blood, skin, lung, breast, and hormone-positive cancers.
2. Gilead Sciences 
Gilead Science is a biopharmaceutical powerhouse that has received many accolades over the years including being named one of Fortune’s Most Admired Companies in the pharmaceutical industry. Gilead Sciences specializes in cancer, hepatitis, and HIV treatments. The firm has a growing oncology department that could integrate the AI diagnosis system to improve its treatment monitoring capabilities.
Gilead has operations in +30 countries and is headquartered in California. The firm currently has +18k employees and is recognized as a leading innovator in cancer treatments. Gilead Science is seen by many as a strong “hold” due to the company’s goal to improve health equity and increase access to life-changing therapies for all.
AI Set to Transform the Medical Industry
The medical field continues to embrace AI which has opened the door for more effective methods and diagnosis. Already there are AI systems that can diagnose diseases, predict Alzheimer’s, and reduce cancer treatment costs. In the future, the AI system could become an in-house healthcare option available to anyone.
AI Lung Cancer Diagnosis Makes Sense
When you examine the results of an AI lung cancer diagnosis study, it’s easy to see why people are excited. The AI can accurately determine tumor cells at lower costs than today’s methods. Additionally, the system was built from day one to continually improve based on new data. As such, this team can be credited for improving millions of lives in the future.
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