Abstract 5777: A guidance platform for nanoparticle mediated photothermal therapy for solid tumors
Bibliographic record
Abstract
Introduction Nanoparticle-mediated Photothermal therapy (PTT) is an option for treating solid tumors with low cost and toxicity. Except for a limited set of cancers, PTT in general has not seen widespread clinical adoption, due in part to lack of effective treatment guidance technology. Recent clinical trials here and elsewhere investigating MRI-guided PTT have highlighted 3 critical requirements, including visualization of (1) tumor nanoparticle uptake, (2) temperature distribution during PTT and (3) treatment effect. These trials also highlighted shortcomings of MRI guidance, including high cost and limited access, complexity, speed and indirect functionality, so there is a need for novel direct, real-time PTT-guidance technology. Methods and Results We have designed and constructed a PTT-guidance platform based on hand-held multispectral photoacoustic (PA) tomography enabled by an in-house built frequency-optimized acoustic sensor array designed specifically to capture volumetric PA signals at depths of up to several cm. This is able to quantitatively image bulk-tissue endogenous and exogeneous contrast, including nanoparticle distribution in tumor (Requirement #1). In addition, this platform provides highly sensitive deep-tissue temperature mapping through the temperature dependence of the tissue Gruneisen parameter, matching or exceeding MRI-based thermometry but with direct, real-time precision in an ergonomic hand-held configuration (Requirement #2). Integration of a custom-built diffuse optical tomography (DOT) module into the PAI platform provides measures of the time-dependent tissue optical properties during PTT, particularly scattering that is known to increase 2 to 3-fold with coagulation above ∼55oC. Thereby, it provides immediate feedback on the size and location of the photocoagulated lesion (Requirement #3). Results of in vivo and pre-clinical validation studies are presented, showing nanoparticle injection and distribution in tissues, PA thermal mapping and DOT-based proof-of-principle monitoring of PTT treatments in patients with localized prostate cancer. Conclusions These studies indicate that we can provide the three needed functionalities for PTT guidance and that, for the first time, this can be done on a spatial scale relevant to clinical translation. This work is supported by the Terry Fox Research Institute and the Canadian Cancer Society. Citation Format: Ivan Kosik, Robert Weersink, Margarete Akens, Fumi Yokote, Sangeet Ghai, Gang Zheng, Brian Wilson. A guidance platform for nanoparticle mediated photothermal therapy for solid tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5777.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".