Cost-Effectiveness of <sup>99m</sup>Tc-Sestamibi in Predicting Response to Chemotherapy in Patients with Lung Cancer: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
UNLABELLED: Multidrug resistance (MDR) is a major problem in lung cancer. (99m)Tc-methoxyisobutylisonitrile ((99m)Tc-MIBI) has been demonstrated to be a noninvasive marker for the diagnosis of MDR-related P glycoprotein and MDR-associated protein expression in various solid tumors. Studies have shown that (99m)Tc-MIBI could play a significant role in the management of lung cancer; for example, it could be used in the selection of patients for chemotherapy or radiotherapy or in combined protocols before the start of treatment. Accurate selection of chemosensitive patients with (99m)Tc-MIBI would result not only in effective treatment of patients but also in significant cost savings for health care providers. There is increasing pressure on health care providers to consider costs in medical decision making, particularly in the last decade, as several economic evaluations have appeared in the medical literature. The aims of this study were to undertake a systematic review of the performance of (99m)Tc-MIBI imaging in the assessment of treatment resistance in lung cancer and to use the findings of the review in a decision tree analysis of the potential cost-effectiveness of (99m)Tc-MIBI imaging in selecting lung cancer patients for chemotherapy. METHODS: This study included a systematic review of the literature and a meta-analysis together with a cost-effectiveness analysis of studies with a decision tree analysis model. RESULTS: Analysis of the studies revealed that the overall sensitivity of (99m)Tc-MIBI in identifying responders to chemotherapy was 94%, the specificity was 90%, and the accuracy was 92%. The sensitivity analysis revealed an incremental cost-effectiveness ratio of greater than pound30,000 ( approximately $42,900) for the strategy of treating all patients to recover the small loss of life expectancy (7.5 d) associated with the use of (99m)Tc-MIBI to preselect patients for chemotherapy. CONCLUSION: (99m)Tc-MIBI SPECT can accurately predict which patients with lung cancer will respond to chemotherapy. The use of (99m)Tc-MIBI to preselect patients for chemotherapy has the potential to yield significant cost savings in the health care system without a significant loss of life expectancy for patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.042 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".