Imaging Pearls ❯ Deep Learning ❯ Liquid Biopsy
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- We found that provider number of years in practice (DATA), awareness of challenges related to MCED testing (DATA), and perceived competence in MCED test use (DATA) were positively and significantly associated with receptivity to MCED test use in practice. An exploratory factor analysis extracted two components: receptivity to MCEDs and awareness of challenges. Surprisingly, these factors had a positive correlation (r = 0.124, p = 0.024). Providers’ perceived competence in using MCED tests and providers’ experience level were significantly associated with receptivity to MCED testing. While there was strong agreement with potential challenges to implementing MCEDs, PCPs were generally receptive to using MCEDs in cancer screening. Keeping PCPs updated on the evolving knowledge of MCEDs is likely critical to building receptivity to MCED testing.
Primary Care Provider Receptivity to Multi-Cancer Early Detection Test Use in Cancer Screening
Christopher V. Chambers et al
J. Pers. Med.2023, 13, 1673. https://doi.org/10.3390/jpm13121673 - We found that PCPs are generally receptive to the idea of incorporating MCED testing into their routine practice for cancer screening. This is in contrast to previous research that found that PCPs had concerns about potential problems associated with genetic testing as a screening tool. In particular, they reported insufficient confidence in their ability to order genetic testing and uncertainty around the clinical benefits of this testing as a screening method in low risk patients . In contrast with other genetic testing, MCEDs appear to have a more clearly defined place in the practice of primary care . MCED testing may represent a role for genetic testing for which PCPs can better understand the management of the results and their ability to explain them to their patients.
Primary Care Provider Receptivity to Multi-Cancer Early Detection Test Use in Cancer Screening
Christopher V. Chambers et al
J. Pers. Med.2023, 13, 1673. https://doi.org/10.3390/jpm13121673 - Not surprisingly, PCPs endorsed many of the items in the survey that related to potential challenges to the introduction of MCED testing into their practice. Several of these related to the amount of time that a discussion of MCED testing and the handling of the results would likely impose on an already busy patient schedule. Others related to concerns about the patient. These included whether patients would complete the additional testing associated with a positive test result and whether insurance would cover these recommended tests and procedures. Previous research has shown that patients often fail to complete recommended follow-up after a positive finding on conventional screening and that these delays may result in a new cancer diagnosis. The cost of the currently available MCED test alone will be outside the reach of many patients.
Primary Care Provider Receptivity to Multi-Cancer Early Detection Test Use in Cancer Screening
Christopher V. Chambers et al
J. Pers. Med.2023, 13, 1673. https://doi.org/10.3390/jpm13121673 - In summary, we found that PCPs in the study were generally receptive to the idea of incorporating MCED testing into their practice of screening for cancer. While they acknowledged the potential challenges to using MCED testing and the additional time that they would need to spend on ordering MCED testing and managing the results, the respondents signaled that they were receptive to MCED testing for cancer screening. Introducing MCED testing into routine screening for cancer will likely mean that the visits with the PCP will take longer or that other trained staff will need to be involvedin the patient education process.
Primary Care Provider Receptivity to Multi-Cancer Early Detection Test Use in Cancer Screening
Christopher V. Chambers et al
J. Pers. Med.2023, 13, 1673. https://doi.org/10.3390/jpm13121673
- Background Emerging blood-based multi-cancer early detection (MCED) tests can detect a variety of cancer types across stages with a range of sensitivity, specificity, and ability to predict the origin of the cancer signal. However, little is known about the general US population’s preferences for MCED tests.
Objective To quantify preferences for MCED tests among US adults aged 50–80 years using a discrete choice experiment (DCE).
Conclusions While there is significant heterogeneity in cancer screening preferences, the majority of participants preferred MCED screening and the accuracy of these tests is important. While the majority of participants preferred adding an MCED test to complement current cancer screenings, the latent class analyses identified a small (16%) and specific subset of individuals who value attributes differently, with particular concern regarding false-negative and false-positive test results, who are significantly less likely to opt-in.
Patient Preferences for Multi‑Cancer Early Detection (MCED) Screening Tests
Heather Gelhorn et al.
The Patient - Patient-Centered Outcomes Research https://doi.org/10.1007/s40271-022-00589-5 - “Offering an MCED screening test as part of the standard of care to individuals between the ages of 50 and 80 years is likely to be well received by the majority of this population. Based on the results of the current study, this could represent a viable approach to population-based cancer screening.”
Patient Preferences for Multi‑Cancer Early Detection (MCED) Screening Tests
Heather Gelhorn et al.
The Patient - Patient-Centered Outcomes Research https://doi.org/10.1007/s40271-022-00589-5 - "Recent advances in Artificial Intelligence (AI) indicate that AI has the potential to enhance how cancer is studied, diagnosed, and treated. In the near future, AI may be able to predict certain clinical outcomes, such as a patient’s response to anti-cancer drugs or combinations of such drugs. Analysis of large datasets using AI may also help discover novel cancer mechanisms, novel biomarkers of therapy response, or uncover novel therapeutic targets in cancer models and cancer patients . For example, tumor cells can be imaged directly in tissue or after being cultured and treated with pharmacological agents, then analyzed using deep-learning tools to unearth features associated with drug response or disease processes such as metastasis. Automatically integrating several disparate data types obtained from patient data, such as radiology images and molecular profiles of blood, may allow AI to improve patient diagnoses and detect cancer earlier than currently possible.”
Artificial intelligence in oncology: From bench to clinic
Jamal Elkhader, Olivier Elemento
Seminars in Cancer Biology 84 (2022) 113–128 - "There are however numerous challenges in developing accurate AI models and implementing them in the research and/or clinical setting. The datasets used to train AI models have started falling under more scrutiny than before as implicit biases in some training datasets have become apparent, the most important of which being lack of ethnic diversity and under-representation of certain groups, e.g. African Americans. Another related challenge is the relative scarcity of datasets that can be used as external validation of AI models, owing to privacy concerns and competition between medical centers that limit data sharing; this in turn hampers the vetting necessary to adopt AI models in clinical environments. Here we review recent advances and opportunities in AI and data science applied to cancer and the challenges AI will need to overcome to thrive in the laboratory and the clinic.”
Artificial intelligence in oncology: From bench to clinic
Jamal Elkhader, Olivier Elemento
Seminars in Cancer Biology 84 (2022) 113–128 - “Machine learning, and especially deep learning, have been used to dramatically enhance research in field of digital pathology. One application of deep learning is the detection and classification of specific cell types in histopathology slides. For example, Sirinukunwattana et al. proposed a deep learning method based on spatially constrained convolutional neural network for detecting and classifying cell nuclei in colon cancer tissue. Qupath, an open-sourced software for digital pathology and whole slide image analysis is capable of detecting nuclei but also comes with a user interface to label histopathology slides and create datasets for training new machine learning models. Within histological slides, deep learning can also be used to detect certain areas of interest and classify specific types of lesions. ”
Artificial intelligence in oncology: From bench to clinic
Jamal Elkhader, Olivier Elemento
Seminars in Cancer Biology 84 (2022) 113–128 - Just like pathology, deep learning is dramatically enhancing the field of radiology. To address the detection and treatment of lung cancer, Hua et al. used a deep learning framework to perform pulmonary nodule classification using CT images from the Lung Image Database Consortium dataset In the case of head and neck cancer, the identification of tumor extranodal extension is known to be difficult to diagnose radiographically, and has previously been diagnosed by postoperative pathology. Kann et al. used a CNN to identify tumor extranodal extension and nodal metastasis, trained on >2000 CT-segmented lymph node samples from patients at the Yale School of Medicine, and tested on 131 samples, achieving an AUC of 0.91 (CI: 0.85 0.97) on both extranodal extension prediction and nodal metastasis prediction. With a >85 % accuracy (extranodal extension: PPV: 0.66, NPV: 0.95; nodal metastasis: PPV: 0.88; 0.82), the use of this model as an identification tool shows favorable potential . ”
Artificial intelligence in oncology: From bench to clinic
Jamal Elkhader, Olivier Elemento
Seminars in Cancer Biology 84 (2022) 113–128 - “Artificial intelligence will continue to play a key role in various facets of the medical field, including drug discovery and clinical decision-making. Implementing artificial intelligence techniques into clinical practice is a promising area, allowing for progress to be made while remaining both vigorous and transparent. With improved imagine diagnostics, the efficient utilization of imaging, molecular, and cellular cancer data to predict clinical outcomes, and providing a catalyst for the development of oncologic drugs, AI has the potential for a powerful transformation. The influx of medical data is likely to continue to blossom as precision medicine continues to be implemented."
Artificial intelligence in oncology: From bench to clinic
Jamal Elkhader, Olivier Elemento
Seminars in Cancer Biology 84 (2022) 113–128