Harnessing Radiomics for Personalized Cancer Treatment: A Comprehensive Insight

Oncology is grappling with treatment-selection uncertainty that conventional imaging has long struggled to settle; radiomics is narrowing that gap, guiding real-time choices across gastrointestinal and hepatobiliary cancers.

Radiomics is changing oncologic decision-making by quantifying imaging signatures that predict diagnosis, prognosis, and treatment response.

That uncertainty is most visible in gastric cancer, where choosing neoadjuvant therapy or upfront surgery often hinges on imperfect predictors; CT radiomics is helping clinicians anticipate key prognostic features and, at the point of decision, shape care pathways, as shown in an MDPI analysis demonstrating decision-impact from CT-derived signatures.

Mechanistically, the same quantitative texture and shape features that forecast prognosis can also tighten diagnostic calls by capturing tumor heterogeneity; in gastric cancer, CT radiomics has been linked to improved diagnostic precision through biomarker-level modeling.

Preoperative staging in cholangiocarcinoma is notoriously uncertain, especially around vascular invasion and resectability; radiomics-driven predictions are beginning to reduce that ambiguity, with CT-based preoperative models predicting invasive behavior to inform surgical planning.

Building on the preoperative decision theme from cholangiocarcinoma, similar radiomic signatures in liver cancers are being used to anticipate invasion, tumor biology, and likely response — not as curiosities, but as inputs to treatment selection.

Carrying that preoperative focus into liver cancer, teams are using quantitative signatures to move from descriptive imaging to triage decisions — who proceeds to resection, ablation, or TACE, and with what surveillance intensity — supported by a JAMA Surgery analysis linking radiomics features to outcomes that shape treatment planning.

If grading or vascular invasion can be inferred noninvasively, treatment pathways can be set with greater confidence; in HCC, DCE‑MRI radiomics has been reported to improve noninvasive assessment relevant to grading and invasion risk, reinforcing the move toward personalized therapy.

Key Takeaways:

• Quantitative imaging is moving from discovery to triage, translating radiomic signatures into who gets surgery, systemic therapy, or locoregional treatment.
• Preoperative uncertainty is a common bottleneck; radiomics tightens both diagnostic and staging calls, particularly around invasion and resectability.
• Modality matters: CT features anchor broad accessibility, while DCE‑MRI augments grading and invasion assessment where available.
• Evidence is coalescing around outcome-linked endpoints (recurrence, survival, pathologic features), enabling more personalized surveillance and therapy.