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Researchers Develop Machine Learning Algorithm to Predict Threatened Miscarriage
A new study shows how routine blood tests and machine learning can help predict the risk of threatened miscarriage early in pregnancy.
As laboratory diagnostics continue to evolve alongside artificial intelligence (AI) and machine learning (ML), a new study out of China highlights the growing potential of routine blood testing as a predictive tool in obstetric care. Researchers have developed a machine learning model capable of identifying early signs of threatened miscarriage using only standard blood test results.
For lab leaders, this is a key opportunity to use existing lab data and infrastructure to support early pregnancy risk detection. Properly standardized blood data can help shift diagnostics upstream, potentially identifying issues before symptoms appear.
A threatened miscarriage (also called threatened abortion) is a medical condition in early pregnancy where a woman experiences vaginal bleeding before 20 weeks gestation, but the cervix remains closed and the pregnancy is still viable—meaning the fetus is alive with a detectable heartbeat.
The results of the study, “Predicting the Risk of Threatened Abortion Using Machine Learning Methods: A Comparative Study,” were published in BMC Pregnancy and Childbirth in August.
To perform the research, scientists from the Second Affiliated Hospital of Shaanxi University of Chinese Medicine, collected medical records and analyzed data from 1,764 patients with threatened miscarriage and 1,489 healthy control subjects. Blood test data of the study participants were collected and a data preprocessing tactic called the Z-score normalization technique, also known as standardization, was applied to routine blood indicators. The high-level, general-purpose programming language called Python was used to facilitate the data transformation of eight different ML algorithms. The performance of those ML models were evaluated by calculating their area under the curve (AUC) values, which represents the overall performance of the various models.
Machine Learning Unlocks Predictive Power of Routine Blood Tests
The ML algorithm known as Deep Neural Network (DNN) achieved the best predictive performance with an AUC value of 96.76% That model also had the top metrics for accuracy at 91.88%, specificity (91.62%) and sensitivity (92.11%).
According to the study, approximately 25% of all pregnancies involve a threatened miscarriage and women who experience this condition are 2.5 times more likely to experience a miscarriage compared to healthy pregnancies.
“We’re not sure why this happens in some pregnancies but not in others,’ said Lisa Jackson, MD, assistant clinical professor in the Raquel and Jaime Gilinski Department of Obstetrics, Gynecology and Reproductive Science at Icahn School of Medicine at Mount Sinai, in an interview with The Bump.
Lisa Jackson, MD, assistant clinical professor in the Raquel and Jaime Gilinski Department of Obstetrics, Gynecology and Reproductive Science at Icahn School of Medicine at Mount Sinai noted, “The good news is that the majority of the time, bleeding in early pregnancy with the presence of fetal heart tones and a closed cervix doesn’t result in miscarriage.” (Photo credit: Icahn School of Medicine at Mount Sinai)
“For most women, the outlook is good, and they go on to have normal pregnancies,” added women’s health expert Jennifer Wider, MD.
Neither Jackson nor Wider were affiliated with the study out of China.
The study links incidents of threatened miscarriage with various factors, including chromosomes, genes, hormonal imbalances, immune factors, and maternal health as well as environmental influences. The researchers also stated in their study, “Women experiencing threatened abortion often encounter significant stress and require multiple clinical assessments to accurately determine their pregnancy status. Threatened abortion not only poses a threat to the health of pregnant women but may also affect their future fertility.”
The researchers believe their ML algorithm has immense clinical value and can assist doctors in accurately identifying high-risk patients. They suggest exploring the application of the DNN model in different clinical settings and patient populations and incorporating more diverse data sources to optimize its predictive capabilities.
A New Role for Clinical Labs in Reproductive Health
The researchers concluded, “Our research on constructing a prediction model for threatened abortion through routine blood tests has revealed the great potential of ML algorithms in detecting threatened abortion. This algorithm is expected to analyze routine blood data to identify at-risk pregnancies at an early stage, significantly improving the early detection of this common pregnancy complication. It will assist healthcare providers in intervening earlier and reducing the incidence of (threatened) abortion.”
More research and trials are needed before any ML model may be used for the early detection of threatened miscarriage incidents. However, this latest study does suggest the potential of machine learning algorithms could be beneficial in timely predictions of the medical condition, which would improve patient outcomes.
For laboratories, this research underscores the untapped value in the routine data already being collected daily. As machine learning models like the deep neural network in this study continue to demonstrate clinical relevance, lab leaders are uniquely positioned to facilitate their integration—by ensuring high-quality, normalized data flows and supporting interdisciplinary collaboration between data scientists and clinicians.
Moving forward, blood testing won’t just confirm conditions, it may soon help predict and prevent them. With strategic investment in data analytics and model validation, labs can transform from diagnostic endpoints into proactive partners in patient care.
—JP Schlingman
New Prostate Cancer Test Uses Machine Learning to Efficiently Spot New Cancer Biomarkers Accurately and Non-Invasively
Researchers in Sweden develop urine test that more effectively screens for prostate cancer than standard PSA test
Clinical laboratories may soon have a new inexpensive, non-invasive urine test to screen for prostate cancer that produces superior results compared to the standard PSA test.
An international team of scientists led by researchers at the Karolinska Institutet in Sweden found they could use machine learning to not only accurately identify the presence of a new set cancer biomarkers in urine samples but also determine the stage or grade of the cancer.
“There are many advantages to measuring biomarkers in urine,” said Mikael Benson, principal researcher in the Department of Clinical Science, Intervention and Technology at Karolinska Institutet and senior investigator for the study, in a news release. “It’s non-invasive and painless and can potentially be done at home. The sample can then be analyzed using routine methods in clinical labs.”
The researchers published their findings in Cancer Research titled, “Combining Spatial Transcriptomics, Pseudotime, and Machine Learning Enables Discovery of Biomarkers for Prostate Cancer.”
“New, more precise biomarkers than PSA can lead to earlier diagnosis and better prognoses for men with prostate cancer,” said Mikael Benson, principal researcher at Karolinska Institutet and senior investigator for the study, in a news release. “Moreover, it can reduce the number of unnecessary prostate biopsies in healthy men.” (Photo copyright: Karolinska Institutet.)
New Prostate Cancer Biomarkers
According to the American Cancer Society, there will be approximately 313,780 new cases of prostate cancer diagnosed this year in the US with about 35,770 deaths due to the disease. About one in eight US men will be diagnosed with prostate cancer in their lifetime, and the lifetime risk of dying from prostate cancer is one in 44 men.
“Early cancer diagnosis is crucial but challenging owing to the lack of reliable biomarkers that can be measured using routine clinical methods. The identification of biomarkers for early detection is complicated by each tumor involving changes in the interactions between thousands of genes. In addition to this staggering complexity, these interactions can vary among patients with the same diagnosis as well as within the same tumor,” the researchers wrote in Cancer Research.
The scientists “hypothesized that reliable biomarkers that can be measured with routine methods could be identified by exploiting three facts:
- The same tumor can have multiple grades of malignant transformation;
- These grades and their molecular changes can be characterized using spatial transcriptomics; and,
- These changes can be integrated into models of malignant transformation using pseudotime models to prioritize the genes that were most correlated with malignant transformation.”
To perform their study, the scientists analyzed the mRNA activity of cells in prostate tumors to construct digital models of prostate cancer. These models were then examined using machine learning, a type of artificial intelligence (AI), to locate specific proteins that could be used as biomarkers.
The researchers evaluated these new biomarkers in urine, blood, and tissue samples from more than 2,000 prostate cancer patients along with a control group. The team’s final calculations found the results of the urine test surpassed the current PSA test traditionally used for diagnosing prostate cancer.
“Prostate cancer can be effectively identified by analyzing the expression of candidate biomarkers in urine,” lead study author Martin Smelik, PhD student at Karolinska Institutet, told Fox News. “This approach outperforms the current blood tests based on PSA, but at the same time keeps the advantages of being non-invasive, painless, and relatively cheap.”
Advancements over Traditional PSA Test
Although the prostate-specific antigen (PSA) test typically used by doctors to diagnose prostate cancer can screen for the disease and monitor its progression, it has limitations.
“While PSA is an incredibly sensitive tool for issues related to the prostate, it is not specific to prostate cancer,” Matthew Abramowitz, MD, associate professor in the Department of Radiation Oncology at the Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, told Fox News. “The techniques proposed in the current study suggest the promise of identifying specific cancer markers in the urine, minimizing some of the specificity concerns associated with PSA.”
“This study highlights the power of machine learning applied to patient data in identifying breakthroughs that can help us diagnose cancer earlier, when our treatments are most effective,” Timothy Showalter, MD, a radiation oncologist at UVA Health in Virginia, told Fox News. “Prostate cancer screening has not seen a transformative advance in decades, and current approaches still rely on the PSA blood test, which is known to have low specificity for clinically significant cancers.”
“Overall, this study demonstrates the diagnostic potential of combining spatial transcriptomics, pseudotime, and machine learning for prostate cancer, which should be further tested in prospective studies,” the researchers wrote.
The Karolinska Institutet team is planning large-scale clinical trials as the next phase of their exploration.
—JP Schlingman
Johns Hopkins Research Team Uses Machine Learning on DNA “Dark Matter” in Blood to Identify Cancer
Findings could lead to new biomarkers clinical laboratories would use for identifying cancer in patients and monitoring treatments
As DNA “dark matter” (the DNA sequences between genes) continues to be studied, researchers are learning that so-called “junk DNA” (non-functional DNA) may influence multiple health conditions and diseases including cancer. This will be of interest to pathologists and clinical laboratories engaged in cancer diagnosis and may lead to new non-invasive liquid biopsy methods for identifying cancer in blood draws.
Researchers at Johns Hopkins Kimmel Cancer Center in Baltimore, Md., developed a technique to identify changes in repeat elements of genetic code in cancerous tissue as well as in cell-free DNA (cf-DNA) that are shed in blood, according to a Johns Hopkins news release.
The Hopkins researchers described their machine learning approach—called ARTEMIS (Analysis of RepeaT EleMents in dISease)—in the journal Science Translational Medicine titled, “Genomewide Repeat Landscapes in Cancer and Cell-Free DNA.”
ARTEMIS “shows potential to predict cases of early-stage lung cancer or liver cancer in humans by detecting repetitive genetic sequences,” Genetic Engineering and Biotechnology News (GEN) reported.
This technique could enable non-invasive monitoring of cancer treatment and cancer diagnosis, Technology Networks noted.
“Our study shows that ARTEMIS can reveal genomewide repeat landscapes that reflect dramatic underlying changes in human cancers,” said study co-leader Akshaya Annapragada (above), an MD/PhD student at the Johns Hopkins University School of Medicine, in a news release. “By illuminating the so-called ‘dark genome,’ the work offers unique insights into the cancer genome and provides a proof-of-concept for the utility of genomewide repeat landscapes as tissue and blood-based biomarkers for cancer detection, characterization, and monitoring.” Clinical laboratories may soon have new biomarkers for the detection of cancer. (Photo copyright: Johns Hopkins University.)
Detecting Early Lung, Liver Cancer
Artemis is a Greek word meaning “hunting goddess.” For the Johns Hopkins researchers, ARTEMIS also describes a technique “to analyze junk DNA found in tumors” and which float in the bloodstream, Financial Times explained.
“It’s like a grand unveiling of what’s behind the curtain,” said geneticist Victor Velculescu, MD, PhD, Professor of Oncology and co-director of the Cancer Genetics and Epigenetics Program at Johns Hopkins Kimmel Cancer Center, in the news release.
“Until ARTEMIS, this dark matter of the genome was essentially ignored, but now we’re seeing that these repeats are not occurring randomly,” he added. “They end up being clustered around genes that are altered in cancer in a variety of different ways, providing the first glimpse that these sequences may be key to tumor development.”
ARTEMIS could “lead to new therapies, new diagnostics, and new screening approaches for cancer,” Velculescu noted.
Repeats of DNA Sequences Tough to Study
For some time technical limitations have hindered analysis of repetitive genomic sequences by scientists.
“Genetic changes in repetitive sequences are a hallmark of cancer and other diseases, but characterizing these has been challenging using standard sequencing approaches,” the study authors wrote in their Science Translational Medicine paper.
“We developed a de novo k-mer (short sequences of DNA)-finding approach called ARTEMIS to identify repeat elements from whole-genome sequencing,” the researchers wrote.
The scientists put ARTEMIS to the test in laboratory experiments.
The first analysis involved 1,280 types of repeating genetic elements “in both normal and tumor tissues from 525 cancer patients” who participated in the Pan-Cancer Analysis of Whole Genomes (PCAWG), according to Technology Networks, which noted these findings:
- A median of 807 altered elements were found in each tumor.
- About two-thirds (820) had not “previously been found altered in human cancer.”
Second, the researchers explored “genomewide repeat element changes that were predictive of cancer,” by using machine learning to give each sample an ARTEMIS score, according to the Johns Hopkins news release.
The scoring detected “525 PCAWG participants’ tumors from the healthy tissues with a high performance” overall Area Under the Curve (AUC) score of 0.96 (perfect score being 1.0) “across all cancer types analyzed,” the Johns Hopkins’ release states.
Liquid Biopsy Deployed
The scientists then used liquid biopsies to determine ARTEMIS’ ability to noninvasively diagnose cancer. Researchers used blood samples from:
- 287 people with and without lung cancer who were part of the Danish Lung Cancer Screening Study, and
- 208 people at risk of liver cancer.
Results, according to Johns Hopkins:
- ARTEMIS classified patients with lung cancer with an AUC of 0.82.
- ARTEMIS detected people with liver cancer, as compared to others with cirrhosis or viral hepatitis, with a score of AUC 0.87.
Finally, the scientists used their “ARTEMIS blood test” to find the origin of tumors in patients with cancer. They reported their technique was 78% accurate in discovering tumor tissue sources among 12 tumor types.
“These analyses reveal widespread changes in repeat landscapes of human cancers and provide an approach for their detection and characterization that could benefit early detection and disease monitoring of patients with cancer,” the researchers wrote in Science Translational Medicine.
Large Clinical Trials Planned
Velculescu said more research is planned, including larger clinical trials.
“While still at an early stage, this research demonstrates how some cancers could be diagnosed earlier by detecting tumor-specific changes in cells collected from blood samples,” Hattie Brooks, PhD, Research Information Manager, Cancer Research UK (CRUK), told Financial Times.
Should ARTEMIS prove to be a viable, non-invasive blood test for cancer, it could provide pathologists and clinical laboratories with new biomarkers and the opportunity to work with oncologists to promptly diagnosis cancer and monitor patients’ response to treatment.
—Donna Marie Pocius
Related Information:
Genomewide Repeat Landscapes in Cancer and Cell-Free DNA
AI Detects Cancer VIA DNA Repeats in Liquid Biopsies
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