lipids analyses with AI

AI-Driven Clinical Data Analysis

Industry: Life Science
Country: Poland
Scope of work: Data Processing & Visualization, Dimensionality Reduction, Statistical Data Modeling, Neural Networks, Model Interpretation
Technology stack: PyTorch, Dash Plotly, SciKit Learn


As the most common type of kidney cancer, clear cell Renal Cell Carcinoma (ccRCC) poses a significant health challenge. At the same time, the study of ccRCC also presents a unique opportunity for medical breakthroughs. 

Renal cell carcinoma kidney cancer is intricately linked with various risk factors like smoking, obesity, and chronic kidney disease. The complexity of RCC, particularly its subtype ccRCC, is further highlighted by its reliance on surgery as the primary treatment, followed by chemotherapy and radiotherapy. This underscores the need for extensive data exploration to improve treatment outcomes. A key aspect of ccRCC is the reprogramming of lipid metabolism, making lipid profiling an essential tool. 

At Right Information, we recognize the profound impact that such malignancies have on patients and the healthcare sector. By integrating complex ‘omic’ data with comprehensive clinical records found on the internet, our team embarked on a task to unravel the complexities of this formidable disease. Our expertise in advanced predictive modeling was key in this process and helped us turn complex datasets into actionable medical knowledge.


Leveraging Clinical Data and AI to Address Clear Cell Challenges

We were confronted with a long table filled with numbers and other records on over 100 patients diagnosed with ccRCC. Central to our AI data analysis were the lipid profiles of these patients, obtained from samples of both tumor and adjacent healthy tissues. It became immediately apparent that traditional methods of analysis would fall short in this case. The voluminous patients’ data spanned over 50 columns, each representing a unique dimension of information.

The multidimensional nature of this data posed a significant challenge. For human researchers, finding correlations and dependencies within this labyrinth of figures is nearly impossible. The sheer volume and complexity made it difficult to extract meaningful insights or see the bigger picture hidden in the numbers.

Our goal in this project was to transform this complexity into clarity. We aimed to reorganize and present the data in a way that would make these numerous dimensions easily readable and interpretable. Our challenge was to create a solution that could navigate this multidimensional data landscape, making it accessible and insightful for those delving into the subtleties of ccRCC tumor analysis.


Enhancing ccRCC Diagnosis through Advanced AI and Lipidomic Data Integration

Right Information decided to develop an advanced predictive AI data model to distinguish between healthy and cancerous tissue. Our aim was to design software that simplified and unified the vast lipidomic data with patient clinical records.

The foundation of our solution for the ccRCC tumor lipidomics study was grounded in ensuring the highest quality of data analysis. Therefore, our first step involved meticulous data cleansing, where we filtered out incomplete and messy records from the database. This purification process was essential to lay a solid foundation for subsequent analyses.

We then embarked on reorganizing the data, aiming to highlight the most crucial features. This step is always critical to prepare the data for deeper analysis and allows to surface valuable insights that would otherwise remain hidden in complex datasets.

Our solution was an exploration tool with interactive data visualization capabilities. The tool embedded on a web page allows users to effortlessly navigate through complex datasets. 

In more detail, the tool enables the following key functionalities:

Interactive clinical data analysis

omic and clinical data integration

The tissue analyses are sets of different lipid samples from the collected sample during surgical procedures. In the graph above, the dots on the left represent patients grouped based on their clinical records. The closer the dots are, the more similarities the algorithm finds. By clicking on these data points, users can see their corresponding tissue laboratory results shown in the visualization on the right. The table below the graphs brings basic information on the selected patients as gathered in the records. 

These data visualizations enable users to identify patterns, correlations, and even outliers in the data, thereby providing invaluable insights into the nature of ccRCC. 

Advanced 2D mapping of high-dimensional data

A key aspect of our approach was mapping all of the high-dimensional data in 2D. We employed 12 different algorithms incorporating statistical techniques and neural networks to transform the intricate data into understandable graphical representations. Each algorithm shows a different perspective.

Users can play with the data by selecting different clinical record highlights, such as the diameter of cancer, the patient’s age, and their BMI, and see the correlation changing in real time.

Our AI data model allows for 2D mapping of high-dimensional data

Training a trusted AI model

Our crowning achievement in this project was the development of an AI model capable of discerning healthy cells from cancerous ones based solely on lipidomic records. A crucial element in implementing artificial intelligence for biomedical purposes is building trust in the model’s decision-making process. One of the features of our software is visualization of the learning process. Users can see how a neural network model is trained to make the decision whether the tissue is cancerous or not. 

The software allows users to see how the model network is trained

After the training, the visualization below on the right shows the red and green areas, which show how the model classifies if the tissue is healthy or tumorous. 

The 2D visualization of model training outcomes

This shows the model’s accuracy in classifying known data points and its ability to generalize knowledge to previously unseen observations.


Revolutionizing ccRCC Management and Research with Advanced Data Science Techniques

Our refined data cleansing and reorganization methods, coupled with innovative data visualization solutions, have provided groundbreaking insights into complex datasets like those involved in ccRCC studies. The AI data model we developed is a clear example of how technology can advance medical research. It distinguishes between healthy and cancerous cells, enabling a proactive approach to managing the disease.

The interactive graphs we provide let users interact with the information, draw their own conclusions, and even come up with new hypotheses. The benefits of our model go beyond just research:

  • Patients can use it to understand how others with similar conditions have responded to treatments, helping them make better decisions about their own care.
  • Medical professionals can study straightforward graphs to find patterns and make informed choices in their practice. This could lead to earlier testing, healthier lifestyle advice, or the exploration of new treatments.

For instance, we noticed that older patients often have higher rates of mortality, possibly due to a later stage of cancer at diagnosis. This finding suggests the need for more frequent screening in older populations. It also raises questions about healthcare trends, such as younger people being more proactive about their health.

The visualization of the model’s learning process confirms its efficacy and reveals potential areas for further improvement, such as addressing the misclassification of outliers. This continuous refinement and evolution of our solutions underscore our dedication to pushing the boundaries of what is possible in medical research and patient care.

In summary, this case study showcases the power of data science services in opening up new possibilities in biomedicine. The methods and technology we use can be applied to other cases, including cancers and diseases, opening the way for more personalized medicine and healthcare innovations.

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