Radiopharmaceutical therapy is entering its measurable era.
The science has arrived. Clinical demand is accelerating. Regulators are asking for dosimetry. What's still missing is the operational software infrastructure — and that gap is closing fast.
Four convergent trends.
Clinical momentum for Lu-177 and alpha-emitters
RPT has moved from niche to category. Academic centers are scaling Lu-177 PSMA and DOTATATE programs and preparing for Ac-225 and other alpha-emitters.
Regulatory focus on dose optimization
Dosage optimization and dosimetry have become explicit regulatory priorities in radiopharmaceutical development — raising the bar for evidence.
Software infrastructure gap
Existing tools are fragmented across vendor silos, legacy packages, and spreadsheets — nothing is yet positioned as the operational layer for scaled RPT.
AI is ready to be useful — carefully
Segmentation, quantification assist, and structured summarization are finally mature enough to deploy inside regulated clinical workflows, as copilots.
From fixed activity to personalized evidence.
RPT delivered as a standardized dose.
Activity was administered according to fixed per-cycle protocols. Dosimetry was largely a research activity — sophisticated, but operationally bolted-on. Dose verification existed, but didn’t scale.
RPT becomes a category.
Large Phase III results and broad clinical adoption of Lu-177 PSMA therapy reshaped the landscape. Programs at academic cancer centers grew rapidly — and the gap between clinical growth and operational infrastructure began to be felt.
Dosimetry becomes a strategic priority.
Regulators, radiopharma developers, and clinical leaders converge around the need for patient-specific absorbed dose estimation — both as a safety tool and as the foundation for personalized treatment.
The infrastructure layer emerges.
Radiopharmaceutical therapy needs the operational software that other clinical modalities already have. RadFox is being built for this moment — dose verification, imaging analytics, and workflow intelligence as a single coherent platform.
Personalized treatment becomes the default.
Cycle-over-cycle dose and response data feeds directly into treatment planning — moving RPT toward the kind of evidence-driven, individualized care that other precision modalities have been building toward for a decade.
What changes when the layer exists.
Fragmented, manual, hard to operationalize.
- —Dosimetry lives in legacy tools and exported spreadsheets
- —Case tracking is informal — lost context between cycles
- —Post-therapy imaging routinely under-analyzed at scale
- —Reports are PDFs; data is not longitudinal
- —Regulatory and audit expectations force overhead
Rigorous, coordinated, operationally usable.
- +Voxel-level dose verification as a routine, auditable workflow
- +Every RPT cycle tracked as a coordinated clinical case
- +Imaging, quantification, and review unified
- +Longitudinal dose + response data as a first-class object
- +Quality-system and audit-ready by design
A narrow window.
The combination of clinical demand, regulatory focus, AI maturity, and the absence of a category-defining platform makes this one of the most important moments to be building software for oncology.