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tissue biopsy; those with negative results may proceed to watchful waiting with follow-up testing. Another
example of a clinically approved algorithm is the risk of malignancy algorithm (ROMA), which utilizes
menopausal status and tumor marker concentrations [ovarian tumor markers CA 125 and human
epididymis secretory protein 4 (HE4)] to assess malignancy risk in women with pelvic masses and identify
those at high risk who should be referred for additional testing to make a definitive diagnosis. The
integration of liquid biopsy into diagnostic algorithms may not only help reduce unnecessary imaging, but
also shift the point at which a diagnostic decision can be made by identifying patients with cancer at earlier
stages, with better prognoses, and quickly providing them with further testing or treatment.

Liquid biopsy could also be used to confirm or refine an initial imaging-based diagnosis. Taking lung cancer
as an example, low-dose computed tomography (LDCT) is a highly sensitive imaging test for detecting
cancer but has a high false positive rate (poor clinical specificity). Adding liquid biopsy to imaging results
could provide additional information to help distinguish between early-stage lung cancer and benign
nodules in high-risk patients with indeterminate pulmonary nodules identified by imaging.

The addition of liquid biopsy to diagnostic algorithms is expected to improve clinical decision making;
however, several challenges need to be considered before blood-based cancer biomarkers can be
incorporated into care. For example, a positive liquid biopsy result may not provide information about the
location of the primary lesion, requiring patients to undergo additional testing. A positive liquid biopsy
result may indicate the presence of cancer before a lesion is visible on imaging; conversely, patients with a
negative result may be directed to watchful waiting. All of these scenarios can create significant concern and
worry for patients and clinicians. Diagnostic algorithms, particularly the plan for follow-up assessment after
liquid biopsy results, need to perform well in terms of accurately identifying the primary lesion or
[22]
eliminating patients without disease after further workup .

Leveraging big data to integrate liquid biopsy into cancer care
The inclusion of liquid biopsy into cancer diagnostic algorithms will require advanced methods that can
integrate various types of data, including results from liquid biopsies, radiomic data, other immunoassays
and clinical chemistry assays, demographic information, and family history. These data may be collected at
a single point in time or serially, or by different providers along the care pathway, adding to the complexity
of data integration and analysis. Advances in digital health, defined as the convergence of digital
technologies with healthcare, may accelerate the translation of complex cancer diagnostic algorithms to
patient care by consolidating various types of patient data to inform clinical decision making.

APPLICATION OF LIQUID BIOPSY TO POPULATION CANCER SCREENING
In many countries, including Japan, China, and Korea, tumor marker panels are now routinely offered by
clinics, along with other preventative health assessments, at annual “wellness checks” to help with early
[23]
identification of cancer . This application of liquid biopsy to cancer screening in the general population
raises concerns about the frequency of false-positive results, which can lead to unnecessary further testing or
invasive procedures . The clinical utility of liquid biopsy for cancer screening could be improved through
[24]
the application of machine learning-based approaches to optimize the interpretation of multi-analyte
screening results [25,26] . The current literature lacks objective published data supporting the effectiveness or
potential economic impact of including multi-analyte cancer biomarker panels in wellness checks; more
studies are needed to determine the best way to integrate liquid biopsy into general health screening
programs.