An Integrated Nanotechnology-Enabled Transbronchial Image-Guided Intervention Strategy for Peripheral Lung Cancer
Bibliographic record
Abstract
Early detection and efficient treatment modality of early-stage peripheral lung cancer is essential. Current nonsurgical treatments for peripheral lung cancer show critical limitations associated with various complications, requiring alternative minimally invasive therapeutics. Porphysome nanoparticle-enabled fluorescence-guided transbronchial photothermal therapy (PTT) of peripheral lung cancer was developed and demonstrated in preclinical animal models. Systemically administered porphysomes accumulated in lung tumors with significantly enhanced disease-to-normal tissue contrast, as confirmed in three subtypes of orthotopic human lung cancer xenografts (A549, H460, and H520) in mice and in an orthotopic VX2 tumor in rabbits. An in-house prototype fluorescence bronchoscope demonstrated the capability of porphysomes for in vivo imaging of lung tumors in the mucosal/submucosal layers, providing real-time fluorescence guidance for transbronchial PTT. Porphysomes also enhanced the efficacy of transbronchial PTT significantly and resulted in selective and efficient tumor tissue ablation in the rabbit model. A clinically used cylindrical diffuser fiber successfully achieved tumor-specific thermal ablation, showing promising evidence for the clinical translation of this novel platform to impact upon nonsurgical treatment of early-stage peripheral lung cancer. Cancer Res; 76(19); 5870-80. ©2016 AACR.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".