Estimate survival fractions and compare fitted response curves. Inspect D10, AUC, and log kill differences. Share clean summaries for quick review and documentation needs.
| Dose | Example Curve A Survival | Example Curve B Survival |
|---|---|---|
| 0.00 | 1.000000 | 1.000000 |
| 2.00 | 0.537944 | 0.571209 |
| 4.00 | 0.227638 | 0.218712 |
| 6.00 | 0.075774 | 0.056135 |
| 8.00 | 0.019841 | 0.009658 |
Linear quadratic survival model: S = exp(-(αD + βD²))
Log cell kill: -log10(S)
D10 dose: Solve αD + βD² = ln(10)
Target dose: Solve αD + βD² = -ln(Target Survival)
Area under curve: Estimated with the trapezoidal rule over the chosen dose range.
Mean survival on range: AUC divided by the selected maximum dose.
Relative difference: ((Curve B Survival - Curve A Survival) / Curve A Survival) × 100
Cell survival curve comparison studies how treatment dose changes the fraction of living cells. It is useful in radiobiology, assay review, and treatment planning. Good comparison goes beyond one percentage. It examines curve shape, target dose, and total survival across a range. This calculator applies the linear quadratic model to two curves. That keeps results consistent and easy to explain. You can review survival at a chosen dose or across a selected interval. You can also estimate log cell kill and D10 values. These outputs support clear reporting and better decisions.
Two curves may appear close at low doses but separate later. Direct comparison exposes that gap. This calculator reports survival fraction for both curves at the selected dose. It also measures absolute difference, percent difference, and survival ratio. Area under the curve summarizes persistence across the range. Mean survival offers another compact summary. D10 shows the dose needed for one log reduction in survival. Target dose comparison shows how much exposure each curve needs to reach the same endpoint. These values help compare sensitivity, resistance, and practical effect.
The linear quadratic model estimates survival as an exponential decay term. Alpha captures linear damage. Beta captures quadratic damage that becomes more important as dose rises. Larger alpha or beta usually lowers survival faster. When a target survival is entered, the calculator solves the positive quadratic root. That gives the estimated dose needed to reach that endpoint. Trapezoidal integration estimates area under each curve from zero to the selected maximum dose. The method stays simple, transparent, and easy to audit.
Use this tool when comparing control and treated groups, alternate protocols, or competing fitted models. Start with alpha and beta values from experiments or literature. Choose a dose range that matches your study design. Then review the summary and dose by dose table. Larger separation in survival and D10 often signals stronger response differences. Lower area under the curve suggests greater overall cell kill. Pair modeled outputs with observed data whenever possible. That improves interpretation and keeps decisions grounded in measurable evidence.
A lower survival fraction means fewer cells remain viable after the selected dose. It usually indicates a stronger damaging effect or higher modeled sensitivity.
Alpha captures linear damage. Beta captures quadratic damage. Together they shape the curve and help describe how survival changes as dose increases.
D10 is the estimated dose needed to reduce survival to 0.1. It is a practical summary of how quickly a curve drops.
The area under the curve summarizes survival across the chosen dose range. A lower value usually means stronger overall cell killing over that interval.
The dose modifying factor compares target doses between two curves. It shows how much more or less dose one curve needs to reach the same survival level.
Yes. Enter fitted alpha and beta values derived from experiments. Then compare modeled survival, target doses, and summary metrics in one place.
Exports help with reporting, review, and record keeping. The CSV file is useful for spreadsheets. The PDF option is useful for quick sharing.
This page compares modeled curves using the linear quadratic equation. It works best after you estimate alpha and beta from observed survival data.