Gynecological cancers are prevalent globally, with the most common of them being ovarian, cervical, and uterine cancer.
Cervical cancer is the fourth most common malignancy among females. Its cases are expected to climb from around 580,000 in 2018 to 700,000 in 2030, with an increase in annual fatalities from 311,000 to 400,000, according to a next-generation gynecological cancer diagnosis report by BIS Research.
The dominant cause of gynecological cancers is genomic aberrations. They cause the tumor to grow excessively within the body. Genomic aberrations from acquired or hereditary mutations and transcriptional abnormalities contribute to gynecological malignancies.
The biomarkers help diagnose, prevent, profile cancer, and provide customized treatments. The genetic and molecular structure profiling of gynecologic malignancies is crucial for cancer diagnosis.
Traditionally, Sanger sequencing analyzed small, single sections of deoxyribonucleic acid (DNA) for cancer profiling. In recent years, advanced molecular genetic testing, such as next-generation sequencing, microarray, and polymerase chain reaction, has made major contributions to decoding the molecular profile of gynecological tumors.
Researchers and clinicians have been able to harness knowledge about these underlying mutations due to advances in sequencing technologies, which have aided stakeholders in the discovery of biomarkers for directing focused, therapeutic decisions.
Here are the current novel treatments and technologies contributing to the effective diagnosis and treatment of gynecological cancer:
Ultrasound to predict ovarian cancer
According to a recent study published on March 21 in the journal Radiology, ovarian lesions on ultrasound can indicate cancer risk. It can help women prevent unnecessary surgery. Ovarian cancer is the deadliest of the gynecologic cancers, affecting around 15,000 women in the U.S. each year.
The new study uses ultrasound pictures to identify adnexal lesions into two categories: classic or non-classic. Classic lesions are often found in fluid-filled cysts and have a low risk. Lesions with a solid structure and blood flow seen on Doppler ultrasound are classified as non-classic lesions.
The ultrasound evaluation of adnexal lesions, or lumps around the uterus, is critical for proper diagnosis since some adnexal lesions can proceed to cancer, while many others are benign and do not require treatment. Adnexal lesions are tumors or mass formed in the uterus, near ovaries, fallopian tubes, or connected tissues. The diagnosis is assessed and improved based on the qualities seen on ultrasound.
Current risk classification systems of the lesions are effective, but their numerous subcategories and multidimensional approach are challenging. Radiologists face difficulties in classification in a busy clinical setting.
The researchers studied 970 isolated adnexal lesions in 878 women with a mean age of 42 years and an average risk of ovarian cancer, indicating they had no family history of the disease or genetic markers associated with it.
Of the 970 lesions, 53 (6%) were cancerous. For identifying malignancy in ovarian cancer, the classic versus non-classic ultrasound-based categorization technique had a sensitivity of 92.5 percent and a specificity of 73.1 percent.
Malignancy was found in less than 1% of tumors having classic ultrasonography characteristics. Lesions have a solid component with blood flow and a malignancy frequency of 32% in the focus group and 50% in study participants over 60 years old.
If more research backs up the findings of the study, the system could become a useful tool for radiologists, saving many women the expenses, stress, and hazards of surgery.
While the results of diagnostic ultrasound scans can help with triaging, ultrasonography has not been demonstrated to be an effective screening tool for ovarian cancer.
Artificial intelligence (AI) for breast cancer diagnosis
A recent study indicates that when paired with radiologist analysis, an artificial intelligence tool trained on about a million screening mammography pictures may spot breast cancer with about 90% accuracy.
The study looked at the potential of a machine learning computer software, a type of AI, to add value to the diagnoses made by a group of 14 radiologists after reviewing 720 mammography images.
The researchers trained their AI algorithm on a large number of photos that matched the results of previous biopsies. Their goal was to make the instrument capable of assisting radiologists in reducing the number of biopsies required in the future.
AI has the potential to improve cancer screening, tumor genetic characterization, drug discovery, and cancer surveillance. Cancer is a complicated and multifaceted disease with thousands of genetic and epigenetic changes. AI-based algorithms have a lot of potential in detecting genetic abnormalities and abnormal protein interactions early on. Modern biomedical research is focused on safely and ethically bringing AI technologies to clinics.
Pilot Digital Diagnostics for Cancer Screening
University Hospital Monklands in Scotland is evaluating artificial intelligence (AI) technology for the early detection of cervical cancer. Every week, the lab at University Hospital Monklands receives 3,500 cervical screening samples from around the country, making it one of the world's first cervical screening programs to use the technology.
The method creates digital images of cervical screening slides from tests that have come back positive for HPV, which is responsible for more than 95 percent of cervical malignancies, using a digital cytology system from Hologic, a women's health firm.
The digital cytology generates digital images of cervical smear slides that have tested positive for the Human Papilloma Virus (HPV). An innovative AI algorithm examines slides and gives the screener an image gallery of the most diagnostically relevant cells, allowing them to discover and diagnose anomalies more quickly and accurately.
Because there would be fewer cells to examine, the approach will aid medical specialists in promptly identifying and properly diagnosing anomalies.
A pilot scheme deploying artificial intelligence and enhanced imaging with digital biomarkers to aid in the diagnosis of cervical cancer is also being tested in Canada.
To summarize, there are many research taking place that will effectively help the medical systems with diagnosis and cures. The hospitals are inculcating new technologies and heading studies in the direction of minimizing human errors. The MedTech industry has now started working toward specialized tools for femtech.
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