Development of transrectal diffuse optical tomography combined with 3D-transrectal ultrasound imaging to monitor the photocoagulation front during interstitial photothermal therapy of primary focal prostate cancer
Bibliographic record
Abstract
Interstitial near-infrared laser thermal therapy (LITT) is currently undergoing clinical trials as an alternative to watchful waiting or radical surgery in patients with low-risk focal prostate cancer. Currently, we use magnetic resonance image (MRI)-based thermography to monitor treatment delivery and determine indirectly the completeness of the target tissue destruction while avoiding damage to adjacent normal tissues, particularly the rectal wall. However, incomplete tumor destruction has occurred in a significant fraction of patients due to premature termination of treatment, since the photocoagulation zone is not directly observed. Hence, we are developing transrectal diffuse optical tomography (TRDOT), in combination with transrectal 3D ultrasound (3D-TRUS), to address his limitation. This is based on the large changes in optical scattering expected upon tissue coagulation. Here, we present forward simulations of a growing coagulated lesion with optical scattering contrast, using an established finite element analysis software platform (NIRFAST). The simulations were validated in tissue-simulating phantoms, with measurements acquired by a state-of-the-art continuous wave (CW) TRDOT system and a recently assembled bench-top CW-DOT system, with specific source-detector configurations. Two image reconstruction schemes were investigated and evaluated, specifically for the accurate delineation of the posterior boundary of the coagulation zone as the critical parameter for treatment guidance in this clinical application.
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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 | 0.001 |
| 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 | 0.001 | 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".