Jun 13,2023

Aptar Digital Health and Tasso Combine Capabilities to Facilitate Blood Sample Collection for Multiple Indications Including Chronic Conditions and Oncology

Aptar Digital Health, a global expert in Software as a Medical Device (SaMD), digital Patient Support Programs (PSPs) and disease management solutions, and Tasso, Inc., the leading provider of patient-centric, clinical-grade blood collection solutions, today entered into a collaboration to offer Aptar Digital Health end-users access to Tasso’s devices for blood collection. The collaboration will focus on improving patient care through a simplified and integrated experience, delivering clinical value to patients through a real-world deployment and increasing adoption of new technologies by patients, providers and pharmaceutical companies. Patients will have access to the Tasso+ and Tasso-M20 devices. The Tasso+ is a U.S. Food and Drug Administration (FDA) Class II 510(k)-cleared blood lancet that collects liquid whole blood samples, and the Tasso-M20, which is CE-marked, collects volumetrically-controlled dried whole blood samples from the patient for delivery to the lab. Onboarded patients will monitor their disease and symptoms through the digital health app provided by Aptar Digital Health and, when a blood test is required by the healthcare provider, samples will be collected with Tasso’s devices and sent for analysis.

COLLABORATION PARTNERSHIP

#connected device

#virtual care

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Aug 18,2023

How futuristic technology is changing the way we monitor people with cancer

Cancer treatments can be incredibly demanding for patients. But as Dr Toby Basey-Fisher, CEO and founder at remote patient monitoring and analytics company Entia explains, new digital technologies are fast changing the landscape of cancer management and making it possible to deliver more effective personalised care for millions of people living with the condition. The article discusses the application of remote patient monitoring, similar to the advancements made in diabetes care, to the field of oncology. It highlights how digital technologies have transformed patient monitoring and improved outcomes in diabetes, and questions whether the same principles can be applied to the more complex world of cancer care.

#rpm

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Aug 21,2023

PicnicHealth partners with AstraZeneca to advance cancer datasets

Health technology company PicnicHealth announced it is moving into the oncology space and teaming up with AstraZeneca to build a registry to generate real-world data on breast cancer. The company says it will work with people living with cancer to collect and structure their medical records data to advance oncology research using robust datasets. Additionally, the California-based company announced a multiyear partnership with biopharmaceutical giant AstraZeneca to create a registry to generate longitudinal real-world data from consenting U.S. patients diagnosed with stage 1 to stage 3 breast cancer. The registry, which patients can currently enroll in, will organize their medical records into a centralized portal and allow patients to contribute their de-identified data to breast cancer research.

COLLABORATION PARTNERSHIP

#data & technology

#rwd

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Aug 17,2023

PathAI Announces Commercial Availability of AISight Digital Pathology Image Management System to Power the Next Generation of Pathology Labs

PathAI, a global leader in AI-powered pathology, today announced the commercial launch of its AISightTM digital pathology image management system (IMS) for anatomic pathology laboratories.1 The launch comes at a time when the pathology space is increasingly transitioning to digitally-enabled reads and recognizing the utility of AI and machine learning to enhance quality control, standardize diagnoses, and quantitate biomarkers. In March of this year, PathAI announced the Early Access Program for AISight, and the 12 leading anatomic pathology laboratories that began trialing the platform. Subsequently, PathAI announced the availability of its first 5 algorithm products through the platform including AIM-PD-L1 algorithms across 4 indications, and the AIM-HER2 Breast Cancer algorithm. In addition to AISight for research use, PathAI also has a regulated platform, AISight™ Dx, that has 510(k) clearance for primary digital diagnosis in the US and is CE-marked in the EU.

PRODUCT

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Aug 07,2023

Dana-Farber AI traces unknown cancers back to their source: study

Researchers at Dana-Farber Cancer Institute have developed an artificial intelligence tool that could help trace a cancer back to its site of origin in particularly tricky cases. While a standard diagnostic work-up can include digital imaging scans, tumor biopsies and pathology reports, clinicians are still unable to find the original source of the malignancy in about 3% to 5% of patients. Known as cancers of unknown primary origin, they are cases of metastatic disease that has spread from a separate and hidden location in the body. The AI program, dubbed OncoNPC, uses sequencing data taken from tumor DNA to help predict where it came from. The computer model was built using medical records and genetic testing results gathered from more than 36,400 patients. OncoNPC was able to accurately predict the origin of about 80% of tumors, including metastatic disease, by linking them with known cancer types, according to Dana-Farber.

CLINICAL STUDY

#ai/software

#ml

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Aug 15,2023

FDA Grants Breakthrough Device Designation To Tempus’ HLA-LOH Companion Diagnostic Test

Tempus today announced that the U.S. Food & Drug Administration (FDA) has granted the company Breakthrough Device Designation for its HLA-LOH assay as a companion diagnostic (CDx) test. The test uses a machine learning model to analyze sequence data produced by Tempus’ FDA-approved, next generation sequencing-based xT CDx assay. It is intended to identify cancer patients with solid tumors who may benefit from treatment with specific targeted therapies when a patient’s tumor has experienced allele-specific loss of heterozygosity (LOH) for specific human leukocyte antigen (HLA) Class I alleles.

REGULATORY FDA

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#ml

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Aug 01,2023

Prediction of metastatic pheochromocytoma and paraganglioma: a machine learning modelling study using data from a cross-sectional cohort

Pheochromocytoma and paraganglioma are rare tumors, and Pheochromocytoma and have up to a 20% rate of metastatic disease that cannot be reliably predicted. This study prospectively assessed whether the dopamine metabolite, methoxytyramine, might predict metastatic disease, whether predictions might be improved using machine learning models that incorporate other features, and how machine learning-based predictions compare with predictions made by specialists in the field. In this machine learning modelling study, they used cross-sectional cohort data from the PMT trial, based in Germany, Poland, and the Netherlands, to prospectively examine the utility of methoxytyramine to predict metastatic disease in 267 patients with pheochromocytoma or paraganglioma and positive biochemical test results at initial screening. The best performing machine learning models were then externally validated using data for all patients in the PMT trial. Results suggested that although methoxytyramine has some utility for prediction of metastatic pheochromocytomas and paragangliomas, sensitivity is limited. Predictive value is considerably enhanced with machine learning models that incorporate the study's nine recommended features.

CLINICAL STUDY

#ml

#ai/software

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Aug 02,2023

AI shows its value in mammography screening

A study has shown that artificial intelligence can be safely used to support mammography to spot breast cancers and may spot more tumours than the current standard approach. The Swedish trial published in The Lancet Oncology found that one expert reader using ScreenPoint's Transpara AI to look at mammograms was at least as good, and possibly better, than the current standard of two expert readers. It is one of the most comprehensive assessments of AI in this setting to date, involving more than 80,000 women attending screening clinics in Sweden who were randomised to either the AI or the control group. Detection rates were six per 1,000 women screened with the AI, compared to five per 1,000 in the control group, with 244 and 203 cases found, respectively. The false-positive rate was 1.5% in both the AI group and the group assessed by two experts. The positive result means that the study will continue to accrue around 100,000 patients, with all subjects followed for at least two years to see if there are any differences between the two groups in the rate of interval cancer – breast cancer found during the three years after a normal result and before the next screening appointment – per 1,000 screens.

CLINICAL STUDY

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Jul 31,2023

Wales expands NHS use of AI for cancer diagnosis

The devolved Welsh Government plans to expand the use of an artificial intelligence-powered tool used to spot cancer in biopsy samples, after seeing a 13% increase in prostate cancer detection in a pilot study. The IBEX Galen AI platform will now be trialled in suspected breast cancer as well as prostate cancer cases, and the initial pilot at the Betsi Cadwaladr Health Board in Bangor, North Wales, will be extended to include five more boards across the country. The Galen Prostate AI – developed by Ibex Medical Analytics – became the first AI-based cancer diagnostic to be given a CE Mark in the EU earlier this year under the In Vitro Diagnostic Medical Devices Regulation (IVDR), and the company is working to get the same status for its breast and gastric cancer algorithms.

COLLABORATION PARTNERSHIP

#institution

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Aug 09,2023

Accurate classification of pulmonary nodules by a combined model of clinical, imaging, and cell-free DNA methylation biomarkers: a model development and external validation study

There is an unmet clinical need for accurate non-invasive tests to facilitate the early diagnosis of lung cancer. Researchers propose a combined model of clinical, imaging, and cell-free DNA methylation biomarkers that aims to improve the classification of pulmonary nodules. They conducted a prospective specimen collection and retrospective masked evaluation study, and recruited participants (1097 enrolled from July 7, 2017, to Feb 12, 2019) with a solitary pulmonary nodule sized 5–30 mm from 24 hospitals across 20 cities in China. They developed a combined clinical and imaging biomarkers (CIBM) model by machine learning for the classification of malignant and benign pulmonary nodules in a cohort (n=839) and validated it in two cohorts (n=258 in the first cohort and n=283 in the second cohort). They then integrated the CIBM model with previously established circulating tumour DNA methylation model (PulmoSeek developed by AnchorDx) to create a new combined model, PulmoSeek Plus (n=258), and verified it in an independent cohort (n=283). The PulmoSeek Plus model had better discrimination capacity compared with the CIBM and PulmoSeek models.

CLINICAL STUDY

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