Theranostics in surgical oncology
Bibliographic record
Abstract
Theranostics is a method where molecules that target surface structures in tumors are coupled with different radioisotopes, allowing them to bind to tumor cells for detection (diagnostic) and elimination (therapeutic). In the case of neuroendocrine tumors (NET), peptides targeting somatostatin receptors (SSTR) are most commonly used for this purpose. These peptides are, for example, coupled with 68 Ga for diagnostic and 177 Lu for therapeutic purposes. This allows SSTR-positive tumors to be detected with high sensitivity and treated effectively, which is particularly beneficial in cases where surgery (alone) is not feasible. However, theranostic procedures can also be used to guide surgical procedures or – in the context of a neoadjuvant approach – increase resectability. Other therapies currently in development aim to increase antitumor effectiveness or aim to combat tumors which are resistant to other radiopharmaceutical therapies (RPT) using new isotopes and SSTR-targeting peptides or combining RPT with other drugs. Modern radiological diagnostics, as well as the production and use of radiopharmaceuticals, require costly equipment and specialized procedures and are therefore not accessible to most patients around the world. However, the expansion of the repertoire of studied and approved theranostics promises to make these highly effective treatments available to more patients. The following review intends to provide an overview of current questions about theranostics with relevance to surgical oncology.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".