Successful treatment of primary renal lymphoma using image guided helical tomotherapy.
Bibliographic record
Abstract
PURPOSE: To describe a clinical pilot case of renal lymphoma successfully treated using helical tomotherapy, and to evaluate alternative hypofractionated treatment schedules and their potential applicability to future cases of renal cell carcinoma (RCC). PATIENTS AND METHODS: An 82-year-old female patient with a large right perinephric mass encircling the lower pole of the right kidney was treated on the Hi-ART unit (TomoTherapy Inc. Madison, WI, USA) with daily pretreatment megavoltage CT imaging. Gross tumor volumes (GTVs) were outlined on every MVCT study. The Planned Adaptive software was used for calculation of dosimetric parameters for both the target and organs at risk (OARs). In response to observed GTV regression, a hypothetical anatomy changes adjusted plan was generated and analyzed. Six alternative treatment schedules were investigated: 48 Gy in 4 and 3 fractions, and 60 Gy in 30, 5, 4 and 3 fractions, as possible clinical scenarios for RCC. Normal tissue complication probability (NTCP) and tumor control probability (TCP) values were estimated for each scenario in the study. RESULTS: During 30 days, the GTV was reduced by 50.6%. The smaller GTV and the reduced planning target volume (PTV) margins from 15 mm to 10 mm after 12 fractions would allow for a decrease of the planned mean liver and spinal cord dose by 3.8 Gy and 4 Gy, respectively. Improvements to portions of the colon include a 3.3 Gy and 9.2 Gy reduction in planned mean dose to the descending and ascending colons, respectively. NTCP and TCP estimates have shown that hypofractionated treatment schedules provide a much higher probability of local control, but the risk of tissue complication rises simultaneously. For this particular case, hypofractionation would not be suitable due to the potential adverse affects brought on to the liver. CONCLUSIONS: Caution should be observed in high dose hypofractionated radiotherapy in right sided, whole kidney carcinoma due to increased risk of liver complication. The accelerated treatment may however be justified by the significantly higher TCP rates for left sided kidney cases. Further investigation of small renal tumors is needed to evaluate control rates, vasculopathy, and residual normal function.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".