Introduction

For decades, cancer treatment was organized mostly by where the disease appeared in the body: breast cancer, lung cancer, prostate cancer. Patients with the same diagnosis often received the same standard therapy, and results varied widely. Some responded well; others did not.

Precision oncology changes this approach. Instead of treating cancer by location alone, doctors look at the molecular and genetic features of an individual patient’s tumor and choose treatments that target those features. Alongside it, theranostics is emerging as one of the most exciting areas of cancer care, combining diagnosis and treatment in a single, connected approach.

In this post, we explain what these fields are, how they work, where AI is helping, and what challenges remain.

What Is Precision Oncology?

Precision oncology (also called personalized oncology) tailors cancer care to the specific biology of a patient’s tumor. It relies on tests that reveal what is driving the cancer’s growth, such as:

• Genomic sequencing: Identifying mutations, fusions, and other genetic changes in tumor DNA
• Biomarker testing: Measuring proteins or other markers (for example, HER2 or PD-L1) that guide therapy choices
• Liquid biopsy: Analyzing circulating tumor DNA from a blood sample, which can help monitor treatment response or detect recurrence with less invasive testing
• Tumor profiling: Combining multiple data types for a fuller picture of the cancer

The results can point to targeted therapies and immunotherapies that are more likely to work for that patient, and help avoid treatments unlikely to help.

Key Treatment Approaches

1. Targeted Therapy

These drugs act on specific molecular abnormalities in cancer cells. Examples include therapies directed at certain EGFR mutations in lung cancer or HER2 in breast cancer.

2. Immunotherapy

Immunotherapies help the patient’s own immune system recognize and attack cancer. Biomarkers can help predict who is more likely to benefit.

3. Tumor-Agnostic Treatments

Some approvals are based on a tumor’s molecular feature rather than its location in the body, reflecting the shift from “where is the cancer?” to “what is driving the cancer?”

4. Combination and Adaptive Strategies

Doctors increasingly combine treatments and adjust them over time as the tumor evolves and resistance develops.

What Is Theranostics?

The word theranostics blends therapy and diagnostics. In oncology, it usually refers to using a matched pair of radiopharmaceuticals:

1. A diagnostic agent carries a radioactive tracer that binds to a specific target on cancer cells. A PET or SPECT scan then shows whether and where the target is present.

2. A therapeutic agent uses the same targeting molecule but carries a therapeutic radioactive payload that delivers radiation directly to those cells.

If the scan shows the target is present, the patient is more likely to benefit from the matching therapy. This “see what you treat, treat what you see” principle is the heart of theranostics.

Well-Known Examples

Neuroendocrine tumors: peptide receptor radionuclide therapy (PRRT) uses lutetium177 DOTATATE, with imaging by somatostatin receptor PET scans.

• Prostate cancer: PSMA-targeted imaging identifies patients whose tumors express PSMA, and lutetium-177 PSMA therapy can then be used in appropriate patients with advanced disease.
• Thyroid cancer: radioactive iodine has been used for many years and is often cited as an early example of theranostic thinking.
• Research continues into new targets, new radioactive isotopes, and use in earlier stages of disease.

How AI Is Supporting Precision Oncology

Precision oncology generates enormous amounts of complex data. AI is increasingly used to help make sense of it:

• Interpreting genomic data: helping identify clinically meaningful variants among thousands of changes
• Imaging analysis: supporting detection, measurement, and tracking of tumors on CT, MRI, and PET scans
• Pathology: assisting analysis of digital pathology slides
• Treatment matching: helping match patients to guidelines, targeted therapies, and clinical trials
• Dosimetry and planning: supporting personalized dose calculations in radiopharmaceutical therapy
• Outcome prediction: exploring models that estimate response or risk, though these need rigorous validation before routine clinical use

Most of these tools are decision support. Oncologists, pathologists, radiologists, and nuclear medicine physicians remain responsible for decisions, usually through a multidisciplinary tumor board.

Benefits for Patient Outcomes

• More personalized treatment: therapy is chosen based on the individual tumor’s
  biology
• Potentially fewer unnecessary treatments: patients may avoid therapies unlikely to
  work, along with their side effects
• Targeted delivery: theranostics aims to concentrate radiation on cancer cells while
  limiting exposure to healthy tissue
• Better monitoring: liquid biopsies and molecular imaging can track response and
  resistance over time
• New options: some patients with advanced disease gain access to treatments that did
  not exist a decade ago

Challenges and Limitations

1. Cost and access: genomic testing, targeted drugs, and radiopharmaceuticals can be
expensive, and availability varies greatly between countries and even between
hospitals.
2. Infrastructure and workforce: theranostics requires specialized facilities, radiation
safety systems, nuclear medicine expertise, and a reliable isotope supply chain.
3. Not every tumor has a target: many patients do not have an actionable alteration, and
even when one is found, benefit is not guaranteed.
4. Drug resistance: tumors can evolve and become resistant to targeted therapies over
time.
5. Data interpretation: genomic results can include variants of uncertain significance,
requiring expert interpretation.
6. Equity and bias: genomic databases and AI models have historically over-represented
certain populations, which may reduce accuracy and benefit for others.
7. Evidence gaps: some newer approaches are still being studied, and patients should
understand which uses are established and which are experimental.

Questions Patients Can AskTheir CareTeam

1. Has my tumor undergone molecular or genomic testing, and what did it show?

2. Are there targeted therapies, immunotherapies, or clinical trials that match my
results?

3. Would molecular imaging or a theranostic approach be appropriate for my type and
stage of cancer?

4. What are the expected benefits, side effects, and alternatives?

5. Can my case be discussed at a multidisciplinary tumor board?

The Road Ahead

Looking toward the coming years, we can expect:

• Wider use of comprehensive genomic profiling and liquid biopsy
• New theranostic targets beyond the currently established cancers, along with
  additional isotopes
• Earlier-line use of radioligand therapies as clinical evidence develops
• Combination approaches that pair theranostics with immunotherapy or targeted drugs
• More validated AI tools integrated into oncology workflows
• Efforts to widen access through training, regional centers, and supply chain
  development

Conclusion

Precision oncology and theranostics represent a major shift in cancer care: moving from
treating a cancer type to treating the specific biology of each patient’s disease, and from
separate diagnosis and treatment to a connected approach where imaging guides
therapy. AI is helping manage the complexity, but the core of progress remains rigorous
science, skilled multidisciplinary teams, and equitable access. For patients, the most
useful step is an informed conversation with their oncology team about which options
apply to them.