• A machine learning approach developed by researchers at MIT’s Koch Institute and Massachusetts General Hospital (MGH) may aid in cancer diagnosis of the unknown primary by examining gene expression programs associated with early cell development and differentiation.
• The scientists focused the model on indicators of disrupted developmental pathways in cancer cells to find a compromise between lowering the number of characteristics while still capturing the most essential information.
• The researchers subsequently created the Developmental Multilayer Perceptron (D-MLP), a machine-learning model that rates a tumor for its developmental components and forecasts its origin.
• After training, the D-MLP was applied to 52 fresh samples of especially difficult malignancies of unknown origin that could not be classified using existing techniques.
• Furthermore, comprehensive comparisons of tumor and embryonic cells in the study offered promising and sometimes surprising insights into the gene expression patterns of different tumor types.

The first stage in deciding the best therapy for a cancer patient is identifying their exact form of cancer, which includes pinpointing the main site, the organ or portion of the body where the disease develops.

Even with rigorous testing, the cause of cancer cannot always be established. Although these cancers of unclear origin are often aggressive, oncologists must treat them with non-targeted medicines, which typically have severe side effects and result in low survival rates.

Using machine learning for cancer diagnosis

A new machine learning methodology developed by researchers at MIT’s Koch Institute for Integrative Cancer Research and Massachusetts General Hospital (MGH) may aid in classifying cancers of the unknown primary by examining gene expression programs associated with early cell development and differentiation.

Salil Garg, a pathologist at MGH and a Charles W. (1955) and Jennifer C. Johnson Clinical Investigator at the Koch Institute, stated: “Sometimes you can apply all the tools that pathologists have to offer, and you are still left without an answer. Machine learning tools like this one could empower oncologists to choose more effective treatments and give more guidance to their patients.”

Garg is the senior author of new research published on August 30 in Cancer Discovery, and the main author is MIT postdoc Enrico Moiso. The artificial intelligence technique has high sensitivity and accuracy in recognizing cancer types.

Parsing the changes in gene expression across various cancers from an unknown source is a great challenge for machine learning to handle. Cancer cells appear and function quite differently than normal cells, due in part to substantial changes in how their genes are expressed.

Advances in single-cell profiling and efforts to classify distinct cell expression patterns in cell atlases have resulted in a plethora of, though sometimes overwhelming, data, including clues to how and where different malignancies started.

Building a machine learning model that utilizes distinctions between healthy and normal cells, as well as between different types of cancer, into a diagnostic tool, on the other hand, is a balancing act. Suppose a very sophisticated model accounts for too many aspects of cancer gene expression. In that case, it may appear to learn the training data flawlessly yet stumble when confronted with fresh data.

However, by simplifying the model by reducing the number of characteristics, the model may lose information that might lead to correct cancer classifications.

Neural network-based visual stimuli classification paves the way for early Alzheimer’s diagnosis

The scientists focused the model on indicators of disrupted developmental pathways in cancer cells to find a compromise between lowering the number of characteristics while still capturing the most essential information. Many pathways direct how cells reproduce, expand, change their form, and move as an embryo grows, as undifferentiated cells specialize into distinct organs.

Cancer cells lose many of their specific characteristics as the tumor grows. At the same time, as they obtain the ability to multiply, change, and metastasize to other tissues, they begin to resemble embryonic cells in certain aspects. Many gene expression programs that control embryogenesis are reactivated or dysregulated in cancer cells.

Creating an ML algorithm that can diagnose cancer

The researchers contrasted the Cancer Genome Atlas (TCGA), which provides gene expression data for 33 tumor types, with the Mouse Organogenesis Cell Atlas (MOCA), which examines 56 distinct trajectories of embryonic cells as they grow and differentiate.

Moiso explains, “Single-cell resolution tools have dramatically changed how we study the biology of cancer, but how we make this revolution impactful for patients is another question. With the emergence of developmental cell atlases, especially ones that focus on early phases of organogenesis such as MOCA, we can expand our tools beyond histological and genomic information and open doors to new ways of profiling and identifying tumors and developing new treatments.”

Researchers found a new ML method capable of counting cells for disease diagnosis

The map of correlations between developmental gene expression patterns in tumors and embryonic cells was then used to train a machine learning algorithm. The researchers divided the gene expression of TCGA tumor samples into discrete components corresponding to a certain moment in a developmental trajectory and assigned a mathematical value to each component. The researchers subsequently created the Developmental Multilayer Perceptron (D-MLP), a machine-learning model that rates a tumor for its developmental components and forecasts its origin.

Following training, the D-MLP was applied to 52 fresh samples of especially difficult malignancies of unknown origin that could not be identified using existing methods. These were the most difficult patients seen at MGH in a four-year period commencing in 2017. Excitingly, the model classified the tumors into four groups and produced forecasts and other data that might aid in diagnosing and treating these patients.

One sample, for example, came from a woman who had a history of breast cancer and had evidence of an aggressive tumor in the fluid spaces surrounding the abdomen. Using the available methods, oncologists could not locate a tumor mass or categorize cancer cells. However, D-MLP strongly predicted ovarian cancer. Six months after the patient originally came, a lump in the ovary was discovered to be the source of the malignancy.

Furthermore, the study’s comprehensive comparisons of tumor and embryonic cells offered promising and sometimes surprising insights into the gene expression profiles of different tumor types. For example, during the early stages of embryonic development, a rudimentary gut tube emerges, with the foregut producing the lungs and other adjacent organs and the mid-and hindgut constituting much of the digestive tract.

Machine learning makes life easier for data scientists

The study found that lung-derived tumor cells had substantial parallels not just to the foregut but also to the mid-and hindgut-derived developmental trajectories. These findings imply that variations in developmental programs may one day be used in the same manner that genetic mutations are frequently used to generate tailored or targeted cancer therapies.

While the work provides a robust technique for tumor classification, it does have certain drawbacks. In the future, researchers intend to improve the prediction value of their model by including more forms of data, namely information from radiography, microscopy, and other types of tumor imaging.

Garg stated: “Developmental gene expression represents only one small slice of all the factors that could be used to diagnose and treat cancers. Integrating radiology, pathology, and gene expression information together is the true next step in personalized medicine for cancer patients.”

One of the most serious difficulties with Alzheimer’s disease is that it is seldom noticed early on when it may be addressed better. Now, a team of researchers at Kaunas University of Technology (KTU) has begun researching how human-computer interfaces might be modified for people with neurologic problems to identify a visible object in front of them.

Classifying visual stimuli

If memory impairment affects the perception of facial features, brain activity data from the visual processing areas of the brain may be used to classify visual stimuli. Rytis Maskeliunas, a researcher at the Department of Multimedia Engineering at KTU, addresses this issue in the study.

“While communicating, the face “tells” us the context of the conversation, especially from an emotional point of view, but can we identify visual stimuli based on brain signals,” said Maskeliūnas. 

The research aimed to assess a person’s capacity to process contextual information from the face and how they react to it.

Maskeliūnas says that several studies show that brain ailments may be diagnosed by looking at facial muscle and eye movements. This is because neurodegenerative diseases damage both memory and cognitive processes as well as the cranial nerve system linked with eye movement.

The study revealed that, in the brain of a person with Alzheimer’s disease, faces are viewed just as individuals without the illness do.

“The study uses data from an electroencephalograph, which measures the electrical impulses in the brain. The brain signals of a person with Alzheimer’s are typically significantly noisier than in a healthy person,” added Dovilė Komolovaitė, the study’s co-author.

This makes it more difficult for the individual to concentrate when suffering from symptoms. But overcoming this problem would make it possible to enable early Alzheimer’s diagnosis. Healthcare is not the only sector making use of augmented data and AI. Did you know that a new neural network is able to read tree heights using satellite images?

How was the experiment conducted?

A group of women over the age of 60 was studied in this research.

“Older age is one of the main risk factors for dementia, and since the effects of gender were noticed in brain waves, the study is more accurate when only one gender group is chosen,” said Komolovaitė.

During an hour-long session, individuals were shown pictures of human faces. The photos were chosen based on a variety of criteria. Neutral and terrified faces were shown when assessing the influence of emotions. Known and random people were displayed when analyzing the familiarity factor.

After each face, participants were asked to push a button to indicate whether the face was inverted or correct.

“Even at this stage, an Alzheimer’s patient makes mistakes, so it is important to determine whether the impairment of the object is due to memory or vision processes,” explained Komolovaitė. 

The research was conducted using conventional electroencephalography equipment, but invasive microelectrodes would provide better data for creating a useful tool. It would allow experts to more accurately measure neuronal activity, which will improve the quality of AI models. If you want to learn more about such models, check out the History of neural networks.

“If we want to use this test as a medical tool, a certification process is also needed,” said Komolovaitė.

“Of course, in addition to the technical requirements, there should be a community environment focused on making life easier for people with Alzheimer’s disease. Still, in my personal opinion, after five years, I think we will still see technologies focused on improving physical function, and the focus on people affected by brain diseases in this field will only come later,” added Maskeliūnas.

Although machine learning (ML) has helped make blood cell counts more accurate and less costly, it has nevertheless been time-consuming because of the need for a lot of manual annotation by humans to train the model. Researchers at Benihang University, on the other hand, have developed a new training method that eliminates much of this work.

The ML method capable of counting cells aims to eliminate human involvement

On April 9, Ambionic’s new training program researchers published their findings in the journal Cyborg and Bionic Systems.

The number and type of blood cells and their count are so important in predicting disease. However, cell analysis techniques for counting blood cells that utilize the detection and measurement of physical and chemical characteristics of cells suspended in a fluid are time-consuming and need complex preparations. Worse still, cell analyzer machines have limited accuracy due to external factors such as temperature, pH, voltage, and magnetic field that may interfere with the equipment. Long story short, researchers have aimed to develop an ML method capable of counting cells. This way, both human involvement will decrease, and estimates will be much more accurate.

Much research into alternatives to replace the current blood testing technique has lately focused on “segmentation” of photographs taken by a high-definition camera connected to a microscope using computer programs. Segmentation entails algorithms that count the number of cells in an image, known as counting pixels.

Convolutional neural networks (CNNs) have been used to identify cells in photos that include only a single type of cell. However, when applied to pictures with many types of cells, they perform rather poorly. So, to address the issue, researchers have turned to CNNs, a kind of machine learning that replicates the human visual cortex’s connection architecture.

To identify a cell, the CNN must first be “trained” to recognize what is and isn’t one on thousands of images of cells that humans have labeled. It then recognizes and can count the cells in a new, unidentified picture.

“But such manual labeling is laborious and expensive, even when done with the assistance of experts,” explained Guangdong Zhan, a co-author of the paper and professor with the Department of Mechanical Engineering and Automation at Beihang University, “which defeats the purpose of an alternative that is supposed to be simpler and cheaper than cell analyzers.”

So the researchers at Beihang University sought to improve the U-Net model, which is a fully convolutional network segmentation model that has been widely utilized in medical image segmentation since it was first developed in 2015. Lately, we’ve learned that ML systems could detect deadly earthquakes swiftly.

The CNN is first trained on a large set of thousands of images containing only one type of cell (mice blood).

Conventional algorithms automatically “preprocess” single-cell-type pictures by reducing noise, improving quality, and detecting the shapes of objects in the photos. After that, they execute adaptive image segmentation. This latter technique determines the various shades of gray in a black and white picture and separates anything that falls outside a certain gray level as a distinct object if it has more than half of its area within this threshold. The adaptive aspect of this approach is that instead of dividing the image into pieces based on a specific gray threshold, it does so based on local features.

The U-Net model is fine-tuned after the single-cell-type training set is fed, using a small collection of manually labeled pictures of various cell types. In contrast, compared with the previous version, which required thousands of images to be labeled by humans, this version only requires 600 instead.

The researchers used a conventional cell analyzer on the same mouse blood samples to evaluate their training strategy. They performed an independent cell count to compare it with their new method. They discovered that their segmentation of multiple-cell-type images training scheme had a 94.85 percent accuracy, which was comparable to that obtained by learning from manually labeled multi-cell-type pictures.

The method may be used on more sophisticated models to tackle complicated segmentation issues. The researchers aim to develop a completely automated algorithm for training machine learning models based on the new training technique, which still involves some degree of manual annotation. The medical industry is not the only benefit of machine learning systems. For instance, researchers used ML to detect Professional Malicious User (PMU) reviews.