Hyperthermia in Cancer Care: A Literature Review
Bibliographic record
Abstract
Introduction: Hyperthermia (HT) in cancer management refers to the external application of heat to raise intratumoural temperature to between 39°C and 45°C. Locoregional hyperthermia (LRHT) is the most used and studied type of HT in cancer care. A literature search was conducted to produce a monograph to help clinicians and patients make informed choices in considering the application of this therapy. Methods: A search was performed in Medline and Cochrane library for LRHT and cancer in May 2020. Eligible studies were English-language clinical studies reporting on efficacy, quality of life (QoL), safety, or feasibility. Additional cursory literature scoping was performed to identify missing papers and background information. Papers were independently screened by two reviewers. Following development of a full monograph, a condensed version suitable for publication was created and is presented here. Results: A total of 980 articles were identified and 166 met inclusion criteria. Most were single-arm or observational. However, among the 166, there were 7 systematic reviews (including 37 RCTs) and 18 additional RCTs identified. Several mechanisms of action have been proposed for HT in cancer care including physiological changes, direct cytotoxic effects, chemosensitization and radiosensitization, and immune modulation. Locoregional HT is used primarily as an adjunct to chemotherapy and radiotherapy due to its possible synergistic effects. Various studies demonstrated improved outcomes for patients treated with LRHT and chemo-and/or-radiotherapy. The best evidence for improved disease control and survival is seen for breast cancer (locally recurrent), cervical cancer, esophageal and gastric cancers, head and neck squamous cell carcinoma, and high-risk soft tissue sarcoma. Research related to quality of life (QoL) is limited and often focuses on pain. Hyperthermia with modern technology and treatment planning is generally well tolerated; the most common side effects are discomfort, mild pain, local erythema, skin burns, and, less commonly, subcutaneous burns. Trial heterogeneity and methodological concerns limit the strength of conclusions. Conclusions: Locoregional HT is a promising adjunct treatment to chemotherapy and radiotherapy for a variety of cancer types. To determine in what situations this therapy could be best applied, more high-quality well-controlled studies are needed.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.017 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".