Radiobiology Video Series. Chapter 06. Radiobiology of Combining Radiotherapy with Other Cancer Treatment Modalities
Bibliographic record
Abstract
In this chapter, we address the role of radiation as treatment modality in the context of oncological treatments given to patients. Physical aspects of the use of ionizing radiation (IR)—by either photons, neutrons, or charged (high linear energy transfer) particles—and their clinical application are summarized. Information is also provided regarding the radiobiological rationale of the use of conventional fractionation as well as alternative fractionation schedules using deviating total dose, fraction size, number of fractions, and the overall treatment time. Pro- and contra arguments of hypofractionation are discussed. In particular, the biological rationale and clinical application of Stereotactic Body Radiation Therapy (SBRT) are described. Furthermore, background information is given about FLASH radiotherapy (RT), which is an emerging new radiation method using ultra-high dose rate allowing the healthy, normal tissues and organs to be spared while maintaining the antitumor effect. Spatial fractionation of radiation in tumor therapy, another method that reduces damage to normal tissue is presented. Normal tissue doses could also be minimized by interstitial or intraluminal irradiation, i.e., brachytherapy, and herein an overview is given on the principles of brachytherapy and its clinical application. Furthermore, details are provided regarding the principles, clinical application, and limitations of boron neutron capture therapy (BNCT). Another important key issue in cancer therapy is the combination of RT with other treatment modalities, e.g., chemotherapy, targeted therapy, immunotherapy, hyperthermia, and hormonal therapy. Combination treatments are aimed to selectively enhance the effect of radiation in cancer cells or to trigger the immune system but also to minimize adverse effects on normal cells. The biological rationale of all these combination treatments as well as their application in clinical settings are outlined. To selectively reach high concentrations of radionuclides in tumor tissue, radioembolization is a highly interesting approach. Also, radioligand therapy which enables specific targeting of cancer cells, while causing minimal harm surrounding healthy tissues is presented. A brief overview is provided on how nanotechnology could contribute to the diagnosis and treatment of cancer. Last but not least, risk factors involved in acquiring secondary tumors after RT are discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.313 | 0.152 |
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".