Theranostic: A Primer for Radiologists
Bibliographic record
Abstract
Theranostic represents a transformative approach in precision medicine, integrating diagnostic imaging with targeted radiopharmaceutical therapy to enhance individualized disease management. Rooted in nuclear medicine, this approach uses molecular targeting agents labelled with diagnostic radioisotopes for imaging and therapeutic radioisotopes for treatment, ensuring a seamless transition from diagnosis to therapy. The field has evolved significantly in the last decade, with prostate-specific membrane antigen (PSMA)-targeted radioligand therapy transforming prostate cancer patient management and somatostatin receptor (SSTR)-targeted agents revolutionizing neuroendocrine tumour (NET) treatment. Considering its interdisciplinary nature, collaboration between nuclear medicine specialists, oncologists, radiologists, and other healthcare professionals is critical to refining clinical applications and improving patient outcomes. As prominent members of the theranostic team, radiologists play a pivotal role, from patient selection and imaging-based eligibility assessment to response evaluation and long-term monitoring. In this regard, advanced imaging modalities facilitate the precise evaluation of disease characteristics, guiding treatment decisions. Hence, as theranostics becomes increasingly integrated into patient management, radiologists face the need to be well-versed in both the technical aspects and clinical implications. In this review, we aimed to provide a primer for radiologists to gain a general insight into the theranostic field and its basic principles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".