The progress and perspectives of terahertz technology for diagnosis of neoplasms: a review
Bibliographic record
Abstract
Abstract Over the past few decades, significant attention has been paid to the biomedical applications of terahertz (THz) technology. Nowadays, THz spectroscopy and imaging have allowed numerous demanding problems in the biological, medical, food, plant and pharmaceutical sciences to be solved. Among the biomedical applications, the label-free diagnosis of malignant and benign neoplasms represents one of the most attractive branches of THz technology. Despite this attractiveness, THz diagnosis methods are still far from being ready for use in medical practice. In this review, we consider modern research results in the THz diagnosis of malignant and benign neoplasms, along with the topical research and engineering problems which restrain the translation of THz technology to clinics. We start by analyzing the common models of THz-wave–tissue interactions and the effects of tissue exposure to THz waves. Then, we discuss the existing modalities of THz spectroscopic and imaging systems, which have either already been applied in medical imaging, or hold strong potential. We summarize the earlier-reported and original results of the THz measurements of neoplasms with different nosology and localization. We pay attention to the origin of contrast between healthy and pathological tissues in the THz spectra and images, and discuss the prospects of THz technology in non-invasive, minimally invasive and intraoperative diagnosis, as well as in aiding histology. Finally, we review the challenging problems of THz diagnosis, as well as attempts to solve them, which should bring THz technology much closer to medical practice. This review allows one to objectively uncover the benefits and weaknesses of THz technology in the diagnosis of malignant and benign neoplasms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".