Python software for analysis of radiation treatment using radiobiological model.
Bibliographic record
Abstract
Introduction : It was a century ago, whether used separately or in combination with other medicines, that radiotherapy evolved into a successful cancer treatment. In traditional radiation therapy, a dose volume histogram (DVH) is utilized for quantitative analysis of the treatment plan after the adoption of the treatment planning system. An isodose distribution is also used for qualitative analysis and evaluation of the treatment plan during this phase. The right treatment plan is assessed using physical and radiobiological models by in-house software developed for radiation oncologists. Materials and Methods: The first process was the OSCAR (Object Scoring with Coloured Area of Regret) treatment planning system, developed by Theratronics International, Kanata, Canada in 1991, which was one of the numerous plan evaluation software programs that were established in the field of radiation medicine to analyse and regularize the dose distribution. Many plan evaluation programs have been created over the developing years, with the commercial software used primarily and the primary inclusion MATLAB, and very few have been created using Microsoft Visual Basic, C++, and Java. As a substitute for MATLAB, Python is utilized because it is freely available and has no commercial value. Results: This study’s primary objective is to personalize the radiobiological effects to predict each patient by using the DVH data, which are now accessible, to improve the overall performance of the prior model. Several programming languages were initially investigated to check the portability and quick program execution to illustrate the novel ideas. It was discovered that Python was adequate for this research even if it has no economic value compared to other languages. The software receives the DVH data in text format as an input, and for convenience, it displays the output using a variety of Python widgets. Discussion: The major radiobiological parameter values, TD50/5, slope parameter (m), and volume parameter (n), are used to calculate the tumor control probability (TCP) and NTCP values of numerous targets and oars from their respective DVH statistics using Python software. The evaluation of the physical indices of the treatment plans, the AAPM, RTOG, and QUANTEC protocols were used in a clinical analysis for the execution of treatment plans. Conclusion: The problem in the previous plan evaluation tool was fixed by the custom-made PYTHON program used for this research investigation, which also added clinical and radiobiological understanding of the treatment plan. The software produces a report using Microsoft Excel for the comprehensive radiobiological and dosimetric plan evaluation study for cancer patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.072 | 0.028 |
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".