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UK Study Claims AI Reading of CT Scans Almost Twice as Accurate at Grading Some Cancers as Clinical Laboratory Testing of Sarcoma Biopsies

Radiological method using AI algorithms to detect, locate, and identify cancer could negate the need for invasive, painful clinical laboratory testing of tissue biopsies

Clinical laboratory testing of cancer biopsies has been the standard in oncology diagnosis for decades. But a recent study by the Institute of Cancer Research (ICR) and the Royal Marsden NHS Foundation Trust in the UK has found that, for some types of sarcomas (malignant tumors), artificial intelligence (AI) can grade the aggressiveness of tumors nearly twice as accurately as lab tests, according to an ICR news release.

This will be of interest to histopathologists and radiologist technologists who are working to develop AI deep learning algorithms to read computed tomography scans (CT scans) to speed diagnosis and treatment of cancer patients.

“Researchers used the CT scans of 170 patients treated at The Royal Marsden with the two most common forms of retroperitoneal sarcoma (RPS)—leiomyosarcoma and liposarcoma—to create an AI algorithm, which was then tested on nearly 90 patients from centers across Europe and the US,” the news release notes.

The researchers then “used a technique called radiomics to analyze the CT scan data, which can extract information about the patient’s disease from medical images, including data which can’t be distinguished by the human eye,” the new release states.

The scientists published their findings in The Lancet Oncology titled, “A CT-based Radiomics Classification Model for the Prediction of Histological Type and Tumor Grade in Retroperitoneal Sarcoma (RADSARC-R): A Retrospective Multicohort Analysis.”

The research team sought to make improvements with this type of cancer because these tumors have “a poor prognosis, upfront characterization of the tumor is difficult, and under-grading is common,” they wrote. The fact that AI reading of CT scans is a non-invasive procedure is major benefit, they added.

Christina Messiou, MD

“This is the largest and most robust study to date that has successfully developed and tested an AI model aimed at improving the diagnosis and grading of retroperitoneal sarcoma using data from CT scans,” said the study’s lead oncology radiologist Christina Messiou, MD, (above), Consultant Radiologist at The Royal Marsden NHS Foundation Trust and Professor in Imaging for Personalized Oncology at The Institute of Cancer Research, London, in a news release. Invasive medical laboratory testing of cancer biopsies may eventually become a thing of the past if this research becomes clinically available for oncology diagnosis. (Photo copyright: The Royal Marsden.)

Study Details

RPS is a relatively difficult cancer to spot, let alone diagnose. It is a rare form of soft-tissue cancer “with approximately 8,600 new cases diagnosed annually in the United States—less than 1% of all newly diagnosed malignancies,” according to Brigham and Women’s Hospital.

In their published study, the UK researchers noted that, “Although more than 50 soft tissue sarcoma radiomics studies have been completed, few include retroperitoneal sarcomas, and the majority use single-center datasets without independent validation. The limited interpretation of the quantitative radiological phenotype in retroperitoneal sarcomas and its association with tumor biology is a missed opportunity.”

According to the ICR news release, “The [AI] model accurately graded the risk—or how aggressive a tumor is likely to be—[in] 82% of the tumors analyzed, while only 44% were correctly graded using a biopsy.”

Additionally, “The [AI] model also accurately predicted the disease type [in] 84% of the sarcomas tested—meaning it can effectively differentiate between leiomyosarcoma and liposarcoma—compared with radiologists who were not able to diagnose 35% of the cases,” the news release states.

“There is an urgent need to improve the diagnosis and treatment of patients with retroperitoneal sarcoma, who currently have poor outcomes,” said the study’s first author Amani Arthur, PhD, Clinical Research Fellow at The Institute of Cancer Research, London, and Registrar at The Royal Marsden NHS Foundation Trust, in the ICR news release.

“The disease is very rare—clinicians may only see one or two cases in their career—which means diagnosis can be slow. This type of sarcoma is also difficult to treat as it can grow to large sizes and, due to the tumor’s location in the abdomen, involve complex surgery,” she continued. “Through this early research, we’ve developed an innovative AI tool using imaging data that could help us more accurately and quickly identify the type and grade of retroperitoneal sarcomas than current methods. This could improve patient outcomes by helping to speed up diagnosis of the disease, and better tailor treatment by reliably identifying the risk of each patient’s disease.

“In the next phase of the study, we will test this model in clinic on patients with potential retroperitoneal sarcomas to see if it can accurately characterize their disease and measure the performance of the technology over time,” Arthur added.

Importance of Study Findings

Speed of detection is key to successful cancer diagnoses, noted Richard Davidson, Chief Executive of Sarcoma UK, a bone and soft tissue cancer charity.

“People are more likely to survive sarcoma if their cancer is diagnosed early—when treatments can be effective and before the sarcoma has spread to other parts of the body. One in six people with sarcoma cancer wait more than a year to receive an accurate diagnosis, so any research that helps patients receive better treatment, care, information and support is welcome,” he told The Guardian.

According to the World Health Organization, cancer kills about 10 million people worldwide every year. Acquisition and medical laboratory testing of tissue biopsies is both painful to patients and time consuming. Thus, a non-invasive method of diagnosing deadly cancers quickly, accurately, and early would be a boon to oncology practices worldwide and could save thousands of lives each year.

—Kristin Althea O’Connor

Related Information:

AI Twice as Accurate as a Biopsy at Grading Aggressiveness of Some Sarcomas

AI Better than Biopsy at Assessing Some Cancers, Study Finds

AI Better than Biopsies for Grading Rare Cancer, New Research Suggests

A CT-based Radiomics Classification Model for the Prediction of Histological Type and Tumor Grade in Retroperitoneal Sarcoma (RADSARC-R): A Retrospective Multicohort Analysis

Stanford Researchers Use Text and Images from Pathologists’ Twitter Accounts to Train New Pathology AI Model

Researchers intend their new AI image retrieval tool to help pathologists locate similar case images to reference for diagnostics, research, and education

Researchers at Stanford University turned to an unusual source—the X social media platform (formerly known as Twitter)—to train an artificial intelligence (AI) system that can look at clinical laboratory pathology images and then retrieve similar images from a database. This is an indication that pathologists are increasingly collecting and storing images of representative cases in their social media accounts. They then consult those libraries when working on new cases that have unusual or unfamiliar features.

The Stanford Medicine scientists trained their AI system—known as Pathology Language and Image Pretraining (PLIP)—on the OpenPath pathology dataset, which contains more than 200,000 images paired with natural language descriptions. The researchers collected most of the data by retrieving tweets in which pathologists posted images accompanied by comments.

“It might be surprising to some folks that there is actually a lot of high-quality medical knowledge that is shared on Twitter,” said researcher James Zou, PhD, Assistant Professor of Biomedical Data Science and senior author of the study, in a Stanford Medicine SCOPE blog post, which added that “the social media platform has become a popular forum for pathologists to share interesting images—so much so that the community has widely adopted a set of 32 hashtags to identify subspecialties.”

“It’s a very active community, which is why we were able to curate hundreds of thousands of these high-quality pathology discussions from Twitter,” Zou said.

The Stanford researchers published their findings in the journal Nature Medicine titled, “A Visual-Language Foundation Model for Pathology Image Analysis Using Medical Twitter.”

James Zou, PhD

“The main application is to help human pathologists look for similar cases to reference,” James Zou, PhD (above), Assistant Professor of Biomedical Data Science, senior author of the study, and his colleagues wrote in Nature Medicine. “Our approach demonstrates that publicly shared medical information is a tremendous resource that can be harnessed to develop medical artificial intelligence for enhancing diagnosis, knowledge sharing, and education.” Leveraging pathologists’ use of social media to store case images for future reference has worked out well for the Stanford Medicine study. (Photo copyright: Stanford University.)

Retrieving Pathology Images from Tweets

“The lack of annotated publicly-available medical images is a major barrier for innovations,” the researchers wrote in Nature Medicine. “At the same time, many de-identified images and much knowledge are shared by clinicians on public forums such as medical Twitter.”

In this case, the goal “is to train a model that can understand both the visual image and the text description,” Zou said in the SCOPE blog post.

Because X is popular among pathologists, the United States and Canadian Academy of Pathology (USCAP), and Pathology Hashtag Ontology project, have recommended a standard series of hashtags, including 32 hashtags for subspecialties, the study authors noted.

Examples include:

“Pathology is perhaps even more suited to Twitter than many other medical fields because for most pathologists, the bulk of our daily work revolves around the interpretation of images for the diagnosis of human disease,” wrote Jerad M. Gardner, MD, a dermatopathologist and section head of bone/soft tissue pathology at Geisinger Medical Center in Danville, Pa., in a blog post about the Pathology Hashtag Ontology project. “Twitter allows us to easily share images of amazing cases with one another, and we can also discuss new controversies, share links to the most cutting edge literature, and interact with and promote the cause of our pathology professional organizations.”

The researchers used the 32 subspecialty hashtags to retrieve English-language tweets posted from 2006 to 2022. Images in the tweets were “typically high-resolution views of cells or tissues stained with dye,” according to the SCOPE blog post.

The researchers collected a total of 232,067 tweets and 243,375 image-text pairs across the 32 subspecialties, they reported. They augmented this with 88,250 replies that received the highest number of likes and had at least one keyword from the ICD-11 codebook. The SCOPE blog post noted that the rankings by “likes” enabled the researchers to screen for high-quality replies.

They then refined the dataset by removing duplicates, retweets, non-pathology images, and tweets marked by Twitter as being “sensitive.” They also removed tweets containing question marks, as this was an indicator that the practitioner was asking a question about an image rather than providing a description, the researchers wrote in Nature Medicine.

They cleaned the text by removing hashtags, Twitter handles, HTML tags, emojis, and links to websites, the researchers noted.

The final OpenPath dataset included:

  • 116,504 image-text pairs from Twitter posts,
  • 59,869 from replies, and
  • 32,041 image-text pairs scraped from the internet or obtained from the LAION dataset.

The latter is an open-source database from Germany that can be used to train text-to-image AI software such as Stable Diffusion.

Training the PLIP AI Platform

Once they had the dataset, the next step was to train the PLIP AI model. This required a technique known as contrastive learning, the researchers wrote, in which the AI learns to associate features from the images with portions of the text.

As explained in Baeldung, an online technology publication, contrastive learning is based on the idea that “it is easier for someone with no prior knowledge, like a kid, to learn new things by contrasting between similar and dissimilar things instead of learning to recognize them one by one.”

“The power of such a model is that we don’t tell it specifically what features to look for. It’s learning the relevant features by itself,” Zou said in the SCOPE blog post.

The resulting AI PLIP tool will enable “a clinician to input a new image or text description to search for similar annotated images in the database—a sort of Google Image search customized for pathologists,” SCOPE explained.

“Maybe a pathologist is looking at something that’s a bit unusual or ambiguous,” Zou told SCOPE. “They could use PLIP to retrieve similar images, then reference those cases to help them make their diagnoses.”

The Stanford University researchers continue to collect pathology images from X. “The more data you have, the more it will improve,” Zou said.

Pathologists will want to keep an eye on the Stanford Medicine research team’s progress. The PLIP AI tool may be a boon to diagnostics and improve patient outcomes and care.

—Stephen Beale

Related Information:

New AI Tool for Pathologists Trained by Twitter (Now Known as X)

A Visual-Language Foundation Model for Pathology Image Analysis Using Medical Twitter

AI + Twitter = Foundation Visual-Language AI for Pathology

Pathology Foundation Model Leverages Medical Twitter Images, Comments

A Visual-Language Foundation Model for Pathology Image Analysis Using Medical Twitter (Preprint)

Pathology Language and Image Pre-Training (PLIP)

Introducing the Pathology Hashtag Ontology

University of Gothenburg Study Findings Affirm Accuracy of Clinical Laboratory Blood Test to Diagnose Alzheimer’s Disease

Already-existing clinical laboratory blood test may be new standard for detecting Alzheimer’s biomarkers

In Sweden, an independent study of an existing blood test for Alzheimer’s disease—called ALZpath—determined that this diagnostic assay appears to be “just as good as, if not surpass, lumbar punctures and expensive brain scans at detecting signs of Alzheimer’s in the brain,” according to a report published by The Guardian.

Alzheimer’s disease is one of the worst forms of dementia and it affects more than six million people annually according to the Alzheimer’s Association. Clinical laboratory testing to diagnose the illness traditionally involves painful, invasive spinal taps and brain scans. For that reason, researchers from the University of Gothenburg in Sweden wanted to evaluate the performance of the ALZpath test when compared to these other diagnostic procedures.

Motivated to seek a less costly, less painful, Alzheimer’s biomarker for clinical laboratory testing, neuroscientist Nicholas Ashton, PhD, Assistant Professor of Neurochemistry at the University of Gothenburg, led a team of scientists that looked at other common biomarkers used to identify changes in the brain of Alzheimer’s patients. That led them to tau protein-based blood tests and specifically to the ALZpath blood test for Alzheimer’s disease developed by ALZpath, Inc., of Carlsbad, Calif.

The researchers published their findings in the journal JAMA Neurology titled, “Diagnostic Accuracy of a Plasma Phosphorylated Tau 217 Immunoassay for Alzheimer Disease Pathology.”

In their JAMA article, they wrote, “the pTau217 immunoassay showed similar accuracies to cerebrospinal fluid biomarkers in identifying abnormal amyloid β (Aβ) and tau pathologies.”

In an earlier article published in medRxiv, Ashton et al wrote, “Phosphorylated tau (pTau) is a specific blood biomarker for Alzheimer’s disease (AD) pathology, with pTau217 considered to have the most utility. However, availability of pTau217 tests for research and clinical use has been limited.”

Thus, the discovery of an existing pTau217 assay (ALZpath) that is accessible and affordable is a boon to Alzheimer’s patients and to the doctors who treat them.

“The ALZpath pTau217 assay showed high diagnostic accuracy in identifying elevated amyloid (AUC, 0.92-0.96; 95%CI 0.89-0.99) and tau (AUC, 0.93-0.97; 95%CI 0.84-0.99) in the brain across all cohorts. These accuracies were significantly higher than other plasma biomarker combinations and equivalent to CSF [cerebrospinal fluid] biomarkers,” an ALZpath press release noted.

“This is an instrumental finding in blood-based biomarkers for Alzheimer’s, paving the way for the clinical use of the ALZpath pTau217 assay,” stated Henrik Zetterberg, MD, PhD (above), Professor of Neurochemistry at the University of Gothenburg and co-author of the study. “This robust assay is already used in multiple labs around the globe.” Clinical laboratories may soon be receiving doctors’ orders for pTau217 blood tests for Alzheimer’s patients. (Photo copyright: University of Gothenburg.)

Study Details

Ashton’s team conducted a cohort study that “examined data from three single-center observational cohorts.” The cohorts included:

“Participants included individuals with and without cognitive impairment grouped by amyloid and tau (AT) status using PET or CSF biomarkers. Data were analyzed from February to June 2023,” the researchers wrote. 

These trials from the US, Canada, and Spain featured 786 participants and featured “either a lumbar puncture or an amyloid PET scan to identify signs of amyloid and tau proteins—hallmarks of Alzheimer’s disease,” The Guardian reported, adding that results of the University of Gothenburg’s study showed that the ALZpath pTau217 blood test “was superior to brain atrophy assessments, in identifying signs of Alzheimer’s.”

“80% of individuals could be definitively diagnosed on a blood test without any other investigation,” Ashton told The Guardian.

Diagnosis Needed to Receive Alzheimer’s Disease Treatments

“If you’re going to receive [the new drugs], you need to prove that you have amyloid in the brain,” Ashton told The Guardian. “It’s just impossible to do spinal taps and brain scans on everyone that would need it worldwide. So, this is where the blood test [has] a huge potential.”

Even countries where such drugs were not yet available (like the UK) would benefit, Ashton said, because the test, “Could potentially say that this is not Alzheimer’s disease and it could be another type of dementia, which would help to direct the patient’s management and treatment routine.”

However, Ashton himself noted the limitations of the new findings—specifically that there is no success shown yet in Alzheimer’s drugs being taken by symptom-free individuals.

“If you do have amyloid in the brain at 50 years of age, the blood test will be positive,” he said. “But what we recommend, and what the guidelines recommend with these blood tests, is that these are to help clinicians—so someone must have had some objective concern that they have Alzheimer’s disease, or [that] their memory is declining,” he told The Guardian.

Experts on the Study Findings

“Blood tests could be used to screen everyone over 50-years old every few years, in much the same way as they are now screened for high cholesterol,” David Curtis, MD, PhD, Honorary Professor in the Genetics, Evolution and Environment department at University College London, told The Guardian.

“Results from these tests could be clear enough to not require further follow-up investigations for some people living with Alzheimer’s disease, which could speed up the diagnosis pathway significantly in future,” Richard Oakley, PhD, Associate Director of Research and Innovation at the Alzheimer’s Society, UK, told The Guardian.

Though Oakley found the findings promising, he pointed out what should come next. “We still need to see more research across different communities to understand how effective these blood tests are across everyone who lives with Alzheimer’s disease,” he said.

“Expanding access to this highly accurate Alzheimer’s disease biomarker is crucial for wider evaluation and implementation of AD blood tests,” the researchers wrote in JAMA Neurology.

“ALZpath makers are in discussions with labs in the UK to launch it for clinical use this year, and one of the co-authors, Henrik Zetterberg, MD, PhD, Professor of Neurochemistry at the University of Gothenburg, is making the assay available for research use as part of the ‘biomarker factory’ at UCL,” The Guardian reported.

In the US, to be prescribed any of the available Alzheimer’s medications, a doctor must diagnose that the patient has amyloid in the brain. A pTau217 diagnostic blood test could be used to make such a diagnosis. Currently, however, the test is only available “for research studies through select partner labs,” Time reported.

“But later this month, doctors in the US will be able to order the test for use with patients. (Some laboratory-developed tests performed by certain certified labs don’t require clearance from the US Food and Drug Administration.),” Time added.

It may be that the University of Gothenburg study will encourage Alzheimer’s doctors in the UK and around the world to consider ordering pTau217 diagnostic blood tests from clinical laboratories, rather than prescribing spinal taps and brains scans for their Alzheimer’s patients.

—Kristin Althea O’Connor

Related Information:

New Study Published in JAMA Neurology Affirms High Diagnostic Accuracy of ALZpath’s pTau217 Test in Identifying Amyloid and Tau in the Brain

Blood Test Could Revolutionize Diagnosis of Alzheimer’s, Experts Say

Simple Blood Tests for Dementia to Be Trialed in NHS

A Blood Test for Alzheimer’s Disease Is Almost Here

Diagnostic Accuracy of a Plasma Phosphorylated Tau 217 Immunoassay for Alzheimer Disease Pathology

Alzheimer’s Disease Facts and Figures

Scientists Develop Blood Test for Alzheimer’s Disease

Rice University Researchers Are Developing an Implantable Cancer Therapeutic Device That May Reduce Cancer Deaths by Half

Immunotherapy device could also enable clinical laboratories to receive in vivo biomarker data wirelessly

Researchers from Rice University in Houston and seven other states in the US are working on a new oncotherapy sense-and-respond implant that could dramatically improve cancer outcomes. Called Targeted Hybrid Oncotherapeutic Regulation (THOR), the technology is intended primarily for the delivery of therapeutic drugs by monitoring specific cancer biomarkers in vivo.

Through a $45 million federal grant from the Advanced Research Projects Agency for Health (ARPA-H), the researchers set out to develop an immunotherapy implantable device that monitors a patient’s cancer and adjusts antibody treatment dosages in real time in response to the biomarkers it measures.

It’s not a far stretch to envision future versions of the THOR platform also being used diagnostically to measure biomarker data and transmit it wirelessly to clinical laboratories and anatomic pathologists.

ARPH-A is a federal funding agency that was established in 2022 to support the development of high-impact research to drive biomedical and health breakthroughs. THOR is the second program to receive funding under its inaugural Open Broad Agency Announcement solicitation for research proposals. 

“By integrating a self-regulated circuit, the THOR technology can adjust the dose of immunotherapy reagents based on a patient’s responses,” said Weiyi Peng, MD, PhD (above), Assistant Professor of Biology and Biochemistry at the University of Houston and co-principal investigator on the research, in a UH press release. “With this new feature, THOR is expected to achieve better efficacy and minimize immune-related toxicity. We hope this personalized immunotherapy will revolutionize treatments for patients with peritoneal cancers that affect the liver, lungs, and other organs.” If anatomic pathologists and clinical laboratories could receive biometric data from the THOR device, that would be a boon to cancer diagnostics. (Photo copyright: University of Houston.)

Antibody Therapy on Demand

Omid Veiseh, PhD, Associate Professor of Bioengineering at Rice University and principal investigator on the project, described the THOR device as a “living drug factory” inside the body. The device is a rod-like gadget that contains onboard electronics and a wireless rechargeable battery. It is three inches long and has a miniaturized bioreactor that contains human epithelial cells that have been engineered to produce immune modulating therapies.

“Instead of tethering patients to hospital beds, IV bags, and external monitors, we’ll use a minimally invasive procedure to implant a small device that continuously monitors their cancer and adjusts their immunotherapy dose in real time,” said Veiseh in a Rice University press release. “This kind of ‘closed-loop therapy’ has been used for managing diabetes, where you have a glucose monitor that continuously talks to an insulin pump.

But for cancer immunotherapy, it’s revolutionary.”

The team believes the THOR device will have the ability to monitor biomarkers and produce an antibody on demand that will trigger the immune system to fight cancer locally. They hope the sensor within THOR will be able to monitor biomarkers of toxicity for the purpose of fine-tuning therapies to a patient immediately in response to signals from a tumor. 

“Today, cancer is treated a bit like a static disease, which it’s not,” Veiseh said. “Clinicians administer a therapy and then wait four to six weeks to do radiological measurements to see if the therapy is working. You lose quite a lot of time if it’s not the right therapy. The tumor may have evolved into a more aggressive form.”

The THOR device lasts 60 days and can be removed after that time. It is designed to educate the immune system to recognize a cancer and prevent it from recurring. If the cancer is not fully eradicated after the first implantation, the patient can be implanted with THOR again. 

Use of AI in THOR Therapy

The researchers plan to spend the next two and a half years building prototypes of the THOR device, testing them in rodents, and refining the list of biomarkers to be utilized in the device. Then, they intend to take an additional year to establish protocols for the US Food and Drug Administration’s (FDA) good manufacturing practices requirements, and to test the final prototype on large animals. The researchers estimate the first human clinical trials for the device will begin in about four years. 

“The first clinical trial will focus on refractory recurrent ovarian cancer, and the benefit of that is that we have an ongoing trial for ovarian cancer with our encapsulated cytokine ‘drug factory’ technology,” said Veiseh in the UH press release. 

The group is starting with ovarian cancer because research in this area is lacking and it will provide the opportunity for THOR to activate the immune system against ovarian cancer, which is typically challenging to fight with immunotherapy approaches. If successful in ovarian cancer, the researchers hope to test THOR in other cancers that metastasize within the abdomen, such as:

All control and decision-making will initially be performed by a healthcare provider based on signals transmitted by THOR using a computer or smartphone. However, Veiseh sees the device ultimately being powered by artificial intelligence (AI) algorithms that could independently make therapeutic decisions.

“As we treat more and more patients [with THOR], the devices are going to learn what type of biomarker readout better predicts efficacy and toxicity and make adjustments based on that,” he predicted. “Between the information you have from the first patient versus the millionth patient you treat, the algorithm is just going to get better and better.”

Moving Forward

In addition to UH and Rice University, scientists working on the project come from several institutions, including:

More research and clinical trials are needed before THOR can be used in the clinical treatment of cancer patients. If the device reaches the commercialization stage, Veiseh plans to either form a new company or license the technology to an existing company for further development.

“We know that the further we advance it in terms of getting that human data, the more likely it is that this could then be transferred to another entity,” he told Precision Medicine Online.

Pathologists and clinical laboratories will want to monitor the progress of the THOR technology’s ability to sense changes in cancer biomarkers and deliver controlled dosages of antibiotic treatments.

—JP Schlingman

Related Information:

UH Researcher on Team Developing Sense-and-Respond Cancer Implant Technology

Feds Fund $45M Rice-Led Research That Could Slash US Cancer Deaths by 50%

$45M Awarded to Develop Sense-and-Respond Implant Technology for Cancer Treatment

Implantable Oncotherapeutic Bioreactor Device Lands $45M Government Funding

ARPA-H Fast Tracks Development of New Cancer Implant Tech

ARPA-H Announces Funding for Programs to Support Cancer Moonshot Objectives

ARPA-H Fast Tracks Development of New Cancer Implant Tech

Feds Investing Nearly $115 Million in Three New Cancer Technology Research Projects

Hopkins Engineers Join $45M Project to Develop Sense-and-Respond Cancer Implant Technology

ARPA-H Projects Aim to Develop Novel Cancer Technologies

Closed-Loop Insulin Delivery Systems: Past, Present, and Future Directions

Researchers Create Artificial Intelligence Tool That Accurately Predicts Outcomes for 14 Types of Cancer

Scientists in Italy Develop Hierarchical Artificial Intelligence System to Analyze Bacterial Species in Culture Plates

New artificial intelligence model agrees with interpretations of human medical technologists and microbiologists with extraordinary accuracy

Microbiology laboratories will be interested in news from Brescia University in Italy, where researchers reportedly have developed a deep learning model that can visually identify and analyze bacterial species in culture plates with a high level of agreement with interpretations made by medical technologists.

They initially trained and tested the system to digitally identify pathogens associated with urinary tract infections (UTIs). UTIs are the source for a large volume of clinical laboratory microbiological testing.

The system, known as DeepColony, uses hierarchical artificial intelligence technology. The researchers say hierarchical AI is better suited to complex decision-making than other approaches, such as generative AI.

The researchers published their findings in the journal Nature titled, “Hierarchical AI Enables Global Interpretation of Culture Plates in the Era of Digital Microbiology.”

In their Nature paper, the researchers explained that microbiologists use conventional methods to visually examine culture plates that contain bacterial colonies. The scientists hypothesize which species of bacteria are present, after which they test their hypothesis “by regrowing samples from each colony separately and then employing mass spectroscopy techniques,” to confirm their hypotheses.

However, DeepColony—which was designed for use with clinical laboratory automation systems—looks at high-resolution digital scans of cultured plates and attempts to identify the bacterial strains and analyze them in much the same way a microbiologist would. For example, it can identify species based on their appearance and determine which colonies are suitable for analysis, the researchers explained.

“Working on a large stream of clinical data, and a complete set of 32 pathogens, the proposed system is capable of effectively assisting plate interpretation with a surprising degree of accuracy in the widespread and demanding framework of urinary tract infections,” the study authors wrote. “Moreover, thanks to the rich species-related generated information, DeepColony can be used for developing trustworthy clinical decision support services in laboratory automation ecosystems from local to global scale.”

Alberto Signoroni, PhD

“Compared to the most common solutions based on single convolutional neural networks (CNN), multi-network architectures are attractive in our case because of their ability to fit into contexts where decision-making processes are stratified into a complex structure,” wrote the study’s lead author Alberto Signoroni, PhD (above), Associate Professor of Computer Science, University of Brescia, and his researcher team in their Nature paper. “The system must be designed to generate useful and easily interpretable information and to support expert decisions according to safety-by-design and human-in-the-loop policies, aiming at achieving cost-effectiveness and skill-empowerment respectively.” Microbiologists and clinical laboratory managers will want to follow the further development of this technology. (Photo copyright: University of Brescia.)

How Hierarchical AI Works

Writing in LinkedIn, patent attorney and self-described technology expert David Cain, JD, of Hauptman Ham, LLP, explained that hierarchical AI systems “are structured in layers, each with its own distinct role yet interconnected in a way that forms a cohesive whole. These systems are significant because they mirror the complexity of human decision-making processes, incorporating multiple levels of analysis and action. This multi-tiered approach allows for nuanced problem-solving and decision-making, akin to a seasoned explorer deftly navigating through a multifaceted terrain.”

DeepColony, the researchers wrote, consists of multiple convolutional neural networks (CNNs) that exchange information and cooperate with one another. The system is structured into five levels—labeled 0 through 4—each handling a different part of the analysis:

  • At level 0, the system determines the number of bacterial colonies and their locations on the plate.
  • At level 1, the system identifies “good colonies,” meaning those suitable for further identification and analysis.
  • At level 2, the system assigns each good colony to a bacterial species “based on visual appearance and growth characteristics,” the researchers wrote, referring to the determination as being “pathogen aware, similarity agnostic.”

The CNN used at this stage was trained by using images of 26,213 isolated colonies comprising 32 bacterial species, the researchers wrote in their paper. Most came from clinical laboratories, but some were obtained from the American Type Culture Collection (ATCC), a repository of biological materials and information resources available to researchers.

  • At level 3, the system attempts to improve accuracy by looking at the larger context of the plate. The goal here is to “determine if observed colonies are similar (pure culture) or different (mixed cultures),” the researchers wrote, describing this step as “similarity aware, pathogen agnostic.” This enables the system to recognize variants of the same strain, the researchers noted, and has the effect of reducing the number of strains identified by the system.

At this level, the system uses two “Siamese CNNs,” which were trained with a dataset of 200,000 image pairs.

Then, at level 4, the system “assesses the clinical significance of the entire plate,” the researchers added. Each plate is labeled as:

  • “Positive” (significant bacterial growth),
  • “No significant growth” (negative), or
  • “Contaminated,” meaning it has three or more “different colony morphologies without a particular pathogen that is prevalent over the others,” the researchers wrote.

If a plate is labeled as “positive,” it can be “further evaluated for possible downstream steps,” using MALDI-TOF mass spectrometry or tests to determine susceptibility to antimicrobial measures, the researchers stated.

“This decision-making process takes into account not only the identification results but also adheres to the specific laboratory guidelines to ensure a proper supportive interpretation in the context of use,” the researchers wrote.

Nearly 100% Agreement with Medical Technologists

To gauge DeepColony’s accuracy, the researchers tested it on a dataset of more than 5,000 urine cultures from a US laboratory. They then compared its analyses with those of human medical technologists who had analyzed the same samples.

Agreement was 99.2% for no-growth cultures, 95.6% for positive cultures, and 77.1% for contaminated or mixed growth cultures, the researchers wrote.

The lower agreement for contaminated cultures was due to “a deliberately precautionary behavior, which is related to ‘safety by design’ criteria,” the researchers noted.

Lead study author Alberto Signoroni, PhD, Associate Professor of Computer Science, University of Brescia, wrote in Nature that many of the plates identified by medical technologists as “contaminated” were labeled as “positive” by DeepColony. “We maximized true negatives while allowing for some false positives, so that DeepColony [can] focus on the most relevant or critical cases,” he said.

Will DeepColony replace medical technologists in clinical laboratories any time soon? Not likely. But the Brescia University study indicates the direction AI in healthcare is headed, with high accuracy and increasing speed. The day may not be far off when pathologists and microbiologists regularly employ AI algorithms to diagnose disease.

—Stephen Beale

Related Information:

Hierarchical AI Enables Global Interpretation of Culture Plates in the Era of Digital Microbiology

Hierarchical Deep Learning Neural Network (HiDeNN): An Artificial Intelligence (AI) Framework for Computational Science and Engineering

An AI System Helps Microbiologists Identify Bacteria

This AI Research Helps Microbiologists to Identify Bacteria

Deep Learning Meets Clinical Microbiology: Unveiling DeepColony for Automated Culture Plates Interpretation

University of Southern California Researchers Develop Vaccine That Boosts Immunity and Helps Patients Avoid Deadly Infections While in Hospitals

New vaccine could give clinical laboratories and antimicrobial stewardship programs the tool they need to dramatically reduce hospital-acquired infections

Healthcare providers and clinical laboratories continue to struggle against hospital-acquired infections (HAIs) and ever-evolving antimicrobial resistant (AMR) bacteria. But now, the University of Southern California (USC) has developed and patented an experimental vaccine that has been shown to protect against so-called “superbugs,” such as methicillin-resistant Staphylococcus aureus (MRSA), an AMR bacteria that causes potentially deadly staph infections in hospitals and other healthcare settings.

The innovative approach focuses on bolstering the patient’s immune system itself, rather than relying on proteins to fight infections, according to a USC Today article. 

Developed by senior study author Brad Spellberg, MD, Chief Medical Officer at the Los Angeles General Medical Center, and colleagues, “The experimental vaccine takes an entirely different approach: It gooses the body’s preexisting supply of pathogen-gobbling immune cells called macrophages, which engulf and digest bacteria, fungi, and other bad actors. These activated fighters, found in all tissues, quickly neutralize incoming invaders which might otherwise multiply rapidly and overwhelm the body’s defenses,” USC Today reported. 

“This is very different from developing new antibiotics,” Jun Yan, a doctoral student at Keck School of Medicine and the study’s first author, told USC Today. “This is using our own immune system to fight against different superbugs, which is a different approach than everybody else.”

To develop the vaccine [the USC researchers] formed a biotechnology startup called ExBaq LLC in Bethesda, Md.

They published their findings in the journal Science Translational Medicine title, “A Protein-Free Vaccine Stimulates Innate Immunity and Protects against Nosocomial Pathogens.”

Ishwar K. Puri, PhD

“The pandemic stimulated unprecedented innovation in vaccine development, where federal funding and university-industry partnerships were game changers for translating promising discoveries from academic labs for the good of all,” said Ishwar K. Puri, PhD (above), senior vice president of research and innovation at USC. “We are both pleased and proud of the critical support the USC Stevens Center provided to enable the development of ExBaq’s experimental vaccine that protects vulnerable populations from serious infections.” Clinical laboratories that work with hospitals in the fight against hospital-acquired infections understand the importance of this discovery. (Photo copyright: University of Southern California.)

USC Vaccine Details

The USC team developed a “protein-free vaccine, composed of aluminum hydroxide, monophosphoryl lipid A, and fungal mannan, that stimulates the innate immune system and confers protection,” the researchers wrote in Science Translational Medicine.

“Tested in two independent labs, the vaccine works within 24 hours and lasts for up to 28 days. In lab models, the number of pathogen-eating immune cells in the blood increased dramatically, and survival time of invasive blood and lung infections improved. Early data suggest that a second dose could extend the window to prevent infection,” USC Today reported.

Unlike anything currently available, the new vaccine focuses on boosting the body itself instead of creating antibodies against certain pathogens. A mere dose of the vaccine is described to “provide rapid protection against nine different bacteria and fungi species,” USC Today noted.

“It’s an early warning system. It’s like Homeland Security putting out a terror alert. Everybody, keep your eyes open. Keep an eye out for suspicious packages. You’re alerting the soldiers and tanks of your immune system. The vaccine activates them,” Spellberg told USC Today

“The vaccine acted through stimulation of the innate, rather than the adaptive, immune system, as demonstrated by efficacy in the absence of lymphocytes that were abrogated by macrophage depletion. A role for macrophages was further supported by the finding that vaccination induced macrophage epigenetic alterations that modulated phagocytosis and the inflammatory response to infection. Together, these data show that this protein-free vaccine is a promising strategy to prevent deadly antimicrobial-resistant healthcare-associated infections,” the researchers wrote in Science Translational Medicine.

Great Need for This Protection

According to the federal Centers for Disease Control and Prevention (CDC), 1.7 million infections and 99,000 deaths are caused by HAIs annually.

“Patients who acquire infections from surgery spend, on average, an additional 6.5 days in the hospital, are five times more likely to be readmitted after discharge and twice as likely to die. Moreover, surgical patients who develop infections are 60% more likely to require admission to a hospital’s intensive care unit. Surgical infections are believed to account for up to 10 billion dollars annually in healthcare expenditures,” the CDC reports.

“All hospitalized patients are susceptible to contracting a [hospital-acquired] infection. Some patients are at greater risk than others: young children, the elderly, and persons with compromised immune systems are more likely to get an infection. Other risk factors are long hospital stays, the use of indwelling catheters, failure of healthcare workers to wash their hands, and overuse of antibiotics,” the CDC notes.

Therefore, USC’s new vaccine may be just what the doctor ordered to protect patients in hospitals and other healthcare settings from deadly HAIs.

Looking Ahead

There are currently no vaccines that are FDA-approved that treat “the most serious antibiotic resistant infections,” USC Today reported.

“Even if there were such vaccines, multiple vaccines would have to be deployed simultaneously to protect against the full slate of antibiotic-resistant microbes that cause healthcare-acquired infections,” Brian Luna, PhD, assistant professor of molecular microbiology and immunology at USC’s Keck School of Medicine, told USC Today

Thus, USC’s new vaccine could be a boon to hospital antimicrobial stewardship programs. But so far, it has only been tested on mice.

“The next step is getting guidance from the US Food and Drug Administration (FDA) on the design of a clinical trial. The first such trial would be done in healthy volunteers to find the right dose of vaccine that is safe and triggers the same kind of immune response in people as seen in the mice,” USC Today reported.

ExBaq LLC has begun talking with potential larger partners who might be willing to help develop the vaccine into clinical testing.

For years hospitals and other healthcare settings—such as long-term care facilities, urgent care clinics, and clinical laboratories—have fought an uphill battle against superbugs. So, for a vaccine to be on the horizon that can prevent life-threatening hospital-acquired infections would be a game changer.

With antimicrobial stewardships being a requirement in all hospitals, medical laboratory managers and microbiologists may celebrate this new development and its potential to be a useful tool in fighting antimicrobial resistant bacteria in their facilities.

—Kristin Althea O’Connor

Related Information:

Superbugs Including MRSA Thwarted by Unconventional Vaccine

A Protein-Free Vaccine Stimulates Innate Immunity and Protects Against Nosocomial Pathogens

Superbug Vaccine “Hulkifies” Macrophages in Mouse Model

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