Clinical Scorecard: Assessing Early Treatment Response to Ablative Radiotherapy in Oligometastatic Cancer: A Comprehensive Review of Radiomics and Machine Learning Approaches

At a Glance

Key Highlights

• OMD represents an intermediate state between localized and widespread cancer.
• ART is a non-invasive alternative to surgery for treating small tumor foci.
• Radiomics and machine learning can enhance prediction of treatment response.
• Failure rates of ART can reach up to 40% in extracranial lesions.
• Methodological quality of studies varies, affecting generalizability.

Guideline-Based Recommendations

Diagnosis

• Identify patients with one to five metastases for potential ART.

Management

• Utilize ART techniques such as SRS, SRT, and SBRT based on lesion characteristics.

Monitoring & Follow-up

• Assess early treatment response within 2-6 months post-ART using radiomic data.

Risks

• Consider the 30-40% failure rates in extracranial lesions when planning treatment.

Patient & Prescribing Data

Adults with brain, bone, lung, or liver metastases.

Combination of ML and DL models with radiomic data may improve predictive accuracy.

Clinical Best Practices

• Incorporate radiomic features into treatment response assessments.
• Utilize validated frameworks like RQS and METRICS for evaluating radiomics studies.
• Ensure transparency in reporting through the CLEAR checklist.

Related Resources & Content

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