May 05,2023

Data supports DermaSensor skin cancer detection device

DermaSensor announced clinical data validating its handheld, non-invasive device for assisting in the detection of skin cancer. In collaboration with Mayo Clinic and the University of Connecticut School of Medicine, the company presented two studies. One demonstrated the standalone performance of DermaSensor’s novel elastic scattering spectroscopy (ESS) device. The other evaluated the impact of the device on primary care physicians’ (PCP) management of skin cancer. DermaSensor’s clinical validation study enrolled 1,005 patients across 22 primary care centers, evaluating 1,579 lesions in total. Dermatopathology evaluation confirmed 224 high-risk lesions, including melanomas, basal cell carcinomas, and squamous cell carcinomas. The ESS device achieved device sensitivity (95.5%) superior to that of the PCPs (83%).

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Apr 29,2023

Findings From Two Large-Scale, Real-World Data Sets Reinforce Clinical Utility of Veracyte’s Decipher Prostate Genomic Classifier

Veracyte, Inc. (Nasdaq: VCYT) announced that new data presented at the American Urological Association (AUA) 2023 Annual Meeting validate the real-world performance and clinical utility of the company’s Decipher Prostate Genomic Classifier. The findings are from two separate, large-scale studies evaluating the Decipher Prostate test among a total of more than 100,000 individuals with prostate cancer and reinforce its role as a new standard of care to help inform treatment decisions for these patients. In the first study presented at AUA2023 (#MP44-17), researchers used data from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database. Researchers linked data from 2,744 patients in the SEER registry who were tested with the Decipher Prostate test between 2016 and 2020. They then quantified the association of these patients’ Decipher Prostate test scores with receipt of surgery, upgrading, upstaging and adverse pathology at RP, and compared these associations to those of tumor volume, PSA, patient age and Gleason score at diagnosis. The result suggests that Decipher Prostate testing at the time of biopsy may improve risk stratification for prostate cancer patients with favorable-risk disease independent of tumor volume. Additionally, the findings suggest that patients with lower Decipher scores who have higher volume tumors may be suitable candidates for conservative management.

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Apr 26,2023

Validation of the Integrated Prediction Model algorithm for outcome of cytoreduction in advanced ovarian cancer

The aim of the study is to validate the Integrated Prediction Model on a retrospective cohort of patients. The researchers previously developed the Integrated Prediction Model using a 4-step algorithm of unresectable stage IVB, patient factors, surgical resectability, and surgical complexity to predict outcome of <1 cm cytoreduction in advanced epithelial ovarian cancer, and triaged patients to neoadjuvant chemotherapy or primary cytoreductive surgery. A retrospective cohort study of 107 patients with advanced ovarian cancer treated between January 2017 and September 2018 was carried out. The study validated the proposal that a triage algorithm integrating patient factors, surgical complexity, and surgical resectability in advanced ovarian cancer had high sensitivity and specificity to predict optimal cytoreduction <1 cm.

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Apr 20,2023

Screening for extranodal extension in HPV-associated oropharyngeal carcinoma: evaluation of a CT-based deep learning algorithm in patient data from a multicentre, randomised de-escalation trial

Pretreatment identification of pathological extranodal extension (ENE) would guide therapy de-escalation strategies for in human papillomavirus (HPV)-associated oropharyngeal carcinoma but is diagnostically challenging. Researchers aimed to evaluate a CT-based deep learning algorithm for prediction of ENE in E3311, a diagnostically challenging cohort wherein algorithm use would be impactful in guiding decision-making. For this retrospective evaluation of deep learning algorithm performance, the research obtained pretreatment CTs and corresponding surgical pathology reports from the multicentre, randomised de-escalation trial E3311. Deep learning algorithm performance for ENE prediction was compared with four board-certified head and neck radiologists. The primary endpoint was the area under the curve (AUC) of the receiver operating characteristic. From 178 collected scans, 313 nodes were annotated: 71 (23%) with ENE in general, 39 (13%) with ENE larger than 1 mm ENE. The deep learning algorithm AUC for ENE classification was 0·86 (95% CI 0·82–0·90), outperforming all readers (p<0·0001 for each).

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Apr 17,2023

New Data Reinforce Ability of Veracyte’s Decipher Prostate Genomic Classifier To Help Identify Prostate Cancer Patients Who Would Benefit from Treatment Intensification

Veracyte, Inc. (Nasdaq: VCYT) announced that new data published in European Urology Oncology suggest the Decipher Prostate Genomic Classifier could help identify prostate cancer patients who have micrometastatic disease (difficult-to-detect tumor cells that extend beyond the prostate) and who may therefore benefit from systemic treatment intensification. The researchers found that there was a significant correlation between patients’ Decipher Prostate scores and the risk of upstaging on PSMA PET, and that high Decipher scores were especially enriched in patients at the highest risk of harboring disease outside their prostate. Accordingly, these patients would be more likely to benefit from systemic treatment intensification as compared to local therapy.

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Apr 13,2023

Creating an Artificial Pathologist

A team from the Max Planck Institute for the Science of Light (MPL) in Erlangen has created a new, fast and precise method for clinicians to analyse cells in tissue samples from cancer patients without the need for a trained pathologist. They use artificial intelligence to evaluate the data their method produces. Using a personalised approach to guide treatment could improve life expectancy, quality of life and reduce unnecessary side effects of cancer patients.

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Apr 10,2023

ChatGPT has Potential to Help Cirrhosis, Liver Cancer Patients

A new study by Cedars-Sinai investigators describes how ChatGPT, an artificial intelligence (AI) chatbot, may help improve health outcomes for patients with cirrhosis and liver cancer by providing easy-to-understand information about basic knowledge, lifestyle and treatments for these conditions. The findings, published in the peer-reviewed journal Clinical and Molecular Hepatology, highlights the AI system’s potential to play a role in clinical practice. Patients diagnosed with liver cancer and cirrhosis, an end-stage liver disease that is also a major risk factor for the most common form of liver cancer, often require extensive treatment that can be complex and challenging to manage. Personalized education AI models could help increase patient knowledge and education, noted Alexander Kuo, MD, medical director of Liver Transplantation Medicine at Cedars-Sinai and co-corresponding author of the study.

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Mar 30,2023

Kaiku Health Digital Patient Monitoring enhances the early detection of evolving peripheral sensory neuropathy and reduces phone call burden

Oulu University Hospital, Vaasa Central Hospital and Elekta (Kaiku Health) have published the first scientific results from a study investigating the use of digital patient monitoring for colorectal cancer (CRC) patients receiving oxaliplatin-based chemotherapy. The study was designed to investigate whether digital patient monitoring could enhance and personalize symptom management as well as improve patient follow-up and empowerment while simplifying care teams’ workflow. In this multicenter trial, Kaiku Health digital patient monitoring platform was provided for CRC patients with advanced disease receiving oxaliplatin-based chemotherapy in the first- or second-line setting or as an adjuvant treatment. Patients received weekly reminders to report on their symptoms through the platform and the care team was notified in the presence of more severe symptoms or unfavorable development. The results generated in the prospective study were compared to a retrospective cohort curated from the patient records at the same institutes. A specific adverse effect of interest in the study was peripheral sensory neuropathy. In the study, earlier detection of peripheral sensory neuropathy (p = 1e?5), did not translate to earlier dose reduction, delays, or unplanned therapy termination compared to the retrospective cohort. In addition, the feasibility of the ePRO follow-up was found to be good with 98% of the patients reporting that the use of the platform was very easy or easy to use.

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Mar 28,2023

Machine Learning Combines with Multispectral Infrared Imaging to Guide Cancer Surgery

Surgical tumor removal remains one of the most common procedures during cancer treatment, with about 45 percent of cancer patients undergoing surgical tumor removal at some point. A recent study published in the Journal of Biomedical Optics and led by Dale J. Waterhouse from University College London, UK, has now proposed such an approach. The research team has developed a new technique that combines machine learning with short-wave infrared (SWIR) fluorescence imaging to detect precise boundaries of tumors. The research team has developed a new technique that combines machine learning with short-wave infrared (SWIR) fluorescence imaging to detect precise boundaries of tumors. Next, they trained seven machine learning models to identify these profiles accurately in multispectral SWIR images. Out of the seven tested models, the best performing model achieved a remarkable per-pixel classification accuracy of 97.5 percent.

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Mar 23,2023

AI Predicts Genetics of Cancerous Brain Tumors in under 90 Seconds

Using artificial intelligence (AI), researchers have discovered how to screen for genetic mutations in cancerous brain tumors in under 90 seconds - and possibly streamline the diagnosis and treatment of gliomas, a study suggests. A team of neurosurgeons and engineers at Michigan Medicine, in collaboration with investigators from New York University, University of California, San Francisco and others, developed an AI-based diagnostic screening system called DeepGlioma that uses rapid imaging to analyze tumor specimens taken during an operation and detect genetic mutations more rapidly. In a study of more than 150 patients with diffuse glioma, the most common and deadly primary brain tumor, the newly developed system identified mutations used by the World Health Organization to define molecular subgroups of the condition with an average accuracy over 90%. The results are published in Nature Medicine.

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