Diagnostic Imaging Modalities to Assess Treatment Response of Bone Metastasis in Patients Receiving Palliative Radiotherapy: A Scoping Review of the Literature
Bibliographic record
Abstract
PURPOSE: Several studies have used objective radiologic data to assess the effect of palliative radiotherapy on tumor burden. The purpose of this literature review was to survey the various metrics that have been used to quantify bone tumor response to palliative radiotherapy by radiographical means and to determine whether any of these metrics were associated with clinical palliative outcomes. METHODS: In accordance with PRISMA extension for Scoping Reviews guidelines, a literature search Ovid Medline and OldMedline from 1946 to February 6, 2019, Embase Classic/Embase from 1947 to 2019 week 5, and Cochrane Central Register of Controlled Trials February 2019 to extract articles related to quantified radiologic evaluation of bone metastases following palliative radiotherapy. Imaging modality, quantification metric, and association between imaging modality and clinical response were recorded. RESULTS: Fourteen articles selected for full-text review utilized computed tomography (10 studies), fluorodeoxyglucose-positron emission tomography (3 studies), magnetic resonance imaging (1 study), diffusion-weighted magnetic resonance imaging (3 studies), and X-ray (1 study) imaging modalities. Variables assessed included tumor volume regression, bone density, metabolic activity, and signal intensity. Studies differed both in the type of imaging modality used and metric derived to quantify the radiologic findings. Fifty percent of the included studies aimed to identify a relationship between a quantified radiologic metric and clinical palliative response. Of these studies, 86% reported a correlation. CONCLUSION: Quantified radiologic metrics can provide an objective measure of response to palliative radiotherapy and may be useful in predicting clinical palliative response. More studies are needed to validate these metrics and develop a standardized protocol for radiologic evaluation that can be implemented into a clinical workflow.
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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 | 0.013 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".