Electrical Impedance Spectroscopy with Multi-modal Tissue Discrimination for Ultrasound-Guided Targeted Biopsy
Bibliographic record
Abstract
Prostate cancer (PCa) diagnosis has undergone a dramatic evolution in recent years.Men with clinical risk factors and important lesions observed via magnetic resonance imaging (MRI) typically undergo a targeted biopsy.In a typical prostate biopsy procedure, the operator uses ultrasound (US) imaging to place a biopsy needle in the lesion(s) identified pre-operation via MRI for sampling.Since the lesion is not always visible in US, there is no way to ascertain that the needle is correctly placed in the lesion before sampling.Thus, negative biopsy results occur often and lead to uncertainty in treatment decisions.This thesis proposes an instrumented needle probe that uses electrical impedance spectroscopy and algorithms to identify the tissue at the needle tip in real-time based on the tissue's bioelectrical properties.A new probe is designed, constructed, and validated on a collected dataset of ex-vivo tissue samples.Two data augmentation methods are proposed to improve the performance of tissue classification algorithms running on small datasets.Lastly, the thesis shows that US waves induce an electric potential in the tissue, which can be effectively measured with the sensorised needle and may provide additional information to classify the tissue.In the future, the use of the sensorised needle for tissue identification could improve confidence in biopsy results, and reduce procedure time and number of biopsies performed.i Components of Chapter 2, at time of writing, are under review as: C. McDermott, H. Asilian-
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".